Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented generation (RAG) and agentic search. Unlike human users, LLMs are constrained by limited attention budgets and are uniquely vulnerable to noise; misleading or irrelevant information is no longer just a nuisance, but a direct cause of hallucinations and reasoning failures. In this perspective paper, we argue that denoising-maximizing usable evidence density and verifiability within a context window-is becoming the primary bottleneck across the full information access pipeline. We conceptualize this paradigm shift through a four-stage framework of IR challenges: from inaccessible to undiscoverable, to misaligned, and finally to unverifiable. Furthermore, we provide a pipeline-organized taxonomy of signal-to-noise optimization techniques, spanning indexing, retrieval, context engineering, verification, and agentic workflow. We also present research works on information denoising in domains that rely heavily on retrieval such as lifelong assistant, coding agent, deep research, and multimodal understanding.

The emergence of generative models enables the creation of texts and images tailored to users' preferences. Existing personalized generative models have two critical limitations: lacking a dedicated paradigm for accurate preference modeling, and generating unimodal content despite real-world multimodal-driven user interactions. Therefore, we propose personalized multimodal generation, which captures modal-specific preferences via a dedicated preference model from multimodal interactions, and then feeds them into downstream generators for personalized multimodal content. However, this task presents two challenges: (1) Gap between continuous preferences from dedicated modeling and discrete token inputs intrinsic to generator architectures; (2) Potential inconsistency between generated images and texts. To tackle these, we present a two-stage framework called Discrete Preference learning for Personalized Multimodal Generation (DPPMG). In the first stage, to accurately learn discrete modal-specific preferences, we introduce a modal-specific graph neural network (a dedicated preference model) to learn users' modal-specific preferences, which preferences are then quantized into discrete preference tokens. In the second stage, the discrete modal-specific preference tokens are injected into downstream text and image generators. To further enhance cross-modal consistency while preserving personalization, we design a cross-modal consistent and personalized reward to fine-tune token-associated parameters. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model in generating personalized and consistent multimodal content.

This paper investigates a novel and critical problem of Graph Cross-Domain Continual Fine-Tuning, which aims to adapt a large pre-trained Graph Foundation Model across diverse domains. Existing continual graph learning methods are mostly limited to incremental settings within only a single domain, and are typically trained from scratch. As a result, they fail to handle cross-domain shifts effectively, suffer from severe forgetting, and lack transferability. To address these challenges, we present G-CORMoL, Graph Continual Fine-tuning with Orthogonal, Router-driven Mixture of LoRA experts. G-CORMoL achieves effective adaptation while preserving prior knowledge by enforcing mathematical orthogonality between expert LoRA adapters, thereby eliminating interference across tasks. It further supports cross-domain knowledge transfer through a symmetric dual-driven routing mechanism that learns a global composition policy over all learned LoRA experts. In addition, it promotes expert specialization via a contrastive objective with theoretical guarantees. Extensive experiments on different cross-domain task orders demonstrate that G-CORMoL achieves robust state-of-the-art performance, not only preventing catastrophic forgetting but also leveraging accumulated knowledge to enable positive transfer.

The rapid development of large language models (LLMs) has significantly enhanced the capabilities of AI-native applications, offering substantial improvements in user experience across various sectors. In particular, the integration of LLMs with external APIs has become critical for services such as Baidu Maps, which leverages ERNIE Bot to provide real-time, intelligent responses through its AI Guide service. However, as user queries diversify, the ability to dynamically discover, design, and integrate new APIs has become increasingly essential. This paper addresses the challenges of automating the real-world API discovery, design, and integration process, focusing on mitigating human labor costs and biases while ensuring the creation of high-quality training data. To this end, we propose an Automatic Real-world API Discovery and Deployment (ARADD) framework to efficiently discover new real-world APIs suitable for query solving and automatically master them with minimal labor cost. Specifically, we firstly propose a Multi-Stage LLM-empowered Iterative Intent Extraction method, which integrates a closed-source LLM with our lightweight agent to capture each new intent accurately and efficiently. Secondly, we propose a Contextual-Aware API Design and Self-Instruct Data Generation module to discover APIs suitable for the captured new intent and generate training data pairs of this intent. Finally, a Two-Stage Data Filtering module is introduced to distill the most influential data point for fine-tuning the agent model. Extensive experiments on a real-world log dataset and the online service side validate the effectiveness of our proposed framework.

Corporate profiling serves as a critical analytical tool for modern enterprises, enabling data-driven decision-making in investment strategies, risk assessment, and strategic planning. It requires integrating quantitative metrics, qualitative insights, and network relationships to capture a company’s role in the business ecosystem. However, traditional methods struggle to synthesize heterogeneous data and model complex interdependencies among corporations, news, and market dynamics, often addressing these aspects in isolation. To address these challenges, this article introduces Financial Graph-based Mixture of Experts Prompt Learning (FGMPL), an innovative framework that unifies graph prompt learning with a multi-task paradigm for corporate profile modeling. The proposed framework reformulates node- and edge-level tasks into a coherent graph-level representation and employs multi-view contrastive learning to effectively integrate textual details with relational structures. Moreover, a novel Financial Multi-Experts Prompting mechanism—with learnable tokens coupled with a Mixture of Experts (MoE) design—is presented to enhance the processing of heterogeneous graph data and bridge the gap between pre-training and downstream tasks. To further improve adaptability, a meta-learning-based prompt tuning strategy is incorporated, enabling rapid transition to various downstream applications. Extensive experiments on real-world financial graphs show that FGMPL consistently outperforms strong pre-training and graph-prompting baselines across corporate performance prediction, relationship prediction, and news classification in both full-data and few-shot settings. In addition, cross-market transfer on a NASDAQ dataset and interpretability/efficiency analyses further demonstrate its robustness and practical applicability.

Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric AI, improving data quality and representation has become essential for enhancing model performance, particularly in applications centered around tabular data. This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement. We provide a systematic review of feature selection methods, which identify and retain the most relevant data attributes, and feature generation approaches, which create new features to simplify the capture of complex data patterns. This survey offers a comprehensive overview of current methodologies through an analysis of recent advancements, practical applications, and the strengths and limitations of these techniques. Finally, we outline open challenges and suggest future perspectives to inspire continued innovation in this field.

In modern cities, there is an increasing trend for the development of business agglomeration, which can foster the prosperity of individual businesses by clustering stores and industries. Recently, the advent of Point-of-Interest (POI) data enables a new paradigm for studying the causal effect of business agglomeration in a data-driven way. To this end, we aim to quantify the contribution of the agglomeration effect to the check-in volume at POIs. This is a non-trivial causal effect estimation task due to the higher-order spatial interference typically exhibited by the agglomeration distribution. Moreover, the confounding bias can be exacerbated due to the complex spatial and functional properties inherent to confounders. Therefore, we propose a Causal effect estimation framework for AgglomeRation Effect (CARE) measurement, which includes a Spatial Interference Diffusion Network (SIDN) and a Disentangled Propensity Estimator (DPE) . SIDN captures spatial interference by spreading the treatment effect among POIs through a dedicated spatial agglomeration hypergraph. Then, DPE models a POI’s propensity of receiving the treatment and further unravels the spatial and inherent aspects of propensity by disentangled learning objectives. In addition, we incorporate SIDN and DPE into a unified causal effect estimation architecture using neural Robinson decomposition. Finally, extensive experiments on three real-world datasets validate the effectiveness and universality of CARE for measuring the agglomeration effect.

Spatial crowdsourcing (SC) is a new form of crowdsourcing that utilizes users (i.e., workers) equipped with smart devices to complete tasks at specific locations. Previous studies usually focus on single task relationships (e.g., dependencies or conflicts) without considering task allocation under multiple relationships. To address this limitation, we jointly consider task dependency and conflict while also considering collaboration among workers for task allocation. In this paper, we define and formulate a new problem, called Joint Dependency and Conflicting Task Allocation in Collaboration-aware Spatial Crowdsourcing (JDCTA), which is proved to be NP-hard. To tackle the JDCTA problem, we first design an approximation algorithm, JDCTA-Greedy, which constructs a set of associated task groups based on task relationships and then greedily allocates these groups, in which we can obtain results with a theoretical bound on the approximate ratio. We then propose JDCTA-Game, a both dependency and conflict aware game approach. JDCTA -Game reduces the strategy space by defining dependency and conflict trees, combined with a dynamic payoff function based on the multiple relationships between tasks, to achieve high-quality solutions. Theoretical analysis demonstrates that this method guarantees the existence of at least one Nash equilibrium, and the solution quality is bounded. Experimental results on both synthetic and real datasets show that our proposed approach outperforms the representative approaches in terms of overall utility.

Recently, frequency transformation (FT) has been increasingly incorporated into deep learning models to significantly enhance state-of-the-art accuracy and efficiency in time series analysis. The advantages of FT, such as high efficiency and a global view, have been rapidly explored and exploited in various time series tasks and applications, demonstrating the promising potential of FT as a new deep learning paradigm for time series analysis. Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT. It is also unclear why FT can enhance time series analysis and what its limitations are in the field. To address these gaps, we present a comprehensive review that systematically investigates and summarizes the recent research advancements in deep learning-based time series analysis with FT. Specifically, we explore the primary approaches used in current models that incorporate FT, the types of neural networks that leverage FT, and the representative FT-equipped models in deep time series analysis. We propose a novel taxonomy to categorize the existing methods in this field, providing a structured overview of the diverse approaches employed in incorporating FT into deep learning models for time series analysis. Finally, we highlight the advantages and limitations of FT for time series modeling and identify potential future research directions that can further contribute to the community of time series analysis.

In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discovery, establishing AI-for-Science (AI4Science) as a dynamic and evolving field. However, there is still a lack of an effective framework for the overall assessment of AI4Science, particularly from a holistic perspective on data quality and model capability. Therefore, in this study, we propose SciHorizon, a comprehensive assessment framework designed to benchmark the readiness of AI4Science from both scientific data and LLM perspectives. First, we introduce a generalizable framework for assessing AI-ready scientific data, encompassing four key dimensions-Quality, FAIRness, Explainability, and Compliance-which are subdivided into 15 sub-dimensions. Drawing on data resource papers published between 2018 and 2023 in peer-reviewed journals, we present recommendation lists of AI-ready datasets for Earth, Life, and Materials Sciences, making a novel and original contribution to the field. Concurrently, to assess the capabilities of LLMs across multiple scientific disciplines, we establish 16 assessment dimensions based on five core indicators-Knowledge, Understanding, Reasoning, Multimodality, and Values-spanning Mathematics, Physics, Chemistry, Life Sciences, and Earth and Space Sciences. Using the developed benchmark datasets, we have conducted a comprehensive evaluation of over 50 representative open-source and closed-source LLMs. All the results are publicly available and can be accessed online at www.scihorizon.cn/en.

Pre-trained Language Models (PLMs), such as ChatGPT, have significantly advanced the field of natural language processing. This progress has inspired a series of innovative studies that explore the adaptation of PLMs to time series analysis, intending to create a unified foundation model that addresses various time series analytical tasks. However, these efforts predominantly focus on Regularly Sampled Time Series (RSTS), neglecting the unique challenges posed by Irregularly Sampled Time Series (ISTS), which are characterized by uneven sampling intervals and prevalent missing data. To bridge this gap, this work takes the first step in exploring the potential of PLMs for ISTS analysis. We begin by investigating the effect of various methods for representing ISTS, aiming to maximize the efficacy of PLMs in the analysis. Furthermore, we propose a unified PLM-based framework, named ISTS-PLM, to address diverse ISTS analytical tasks. It integrates novel time-aware and variable-aware PLMs tailored to tackle the intractable intra- and inter-time series modeling in ISTS. Finally, extensive experiments on a comprehensive benchmark demonstrate that the ISTS-PLM, utilizing a structured and effective series-based representation for ISTS, consistently achieves state-of-the-art performance across various analytical tasks, such as classification, interpolation, extrapolation, few-shot and zero-shot learning scenarios, spanning scientific domains like healthcare, biomechanics, and climate science.

Proteins, as essential biomolecules, play a central role in biological processes, including metabolic reactions and DNA replication. Accurate prediction of their properties and functions is crucial in biological applications. Recent development of protein language models (pLMs) with supervised fine tuning provides a promising solution to this problem. However, the fine-tuned model is tailored for particular downstream prediction task, and achieving general-purpose protein understanding remains a challenge. In this paper, we introduce Structure-Enhanced Protein Instruction Tuning (SEPIT) framework to bridge this gap. Our approach incorporates a novel structure-aware module into pLMs to enrich their structural knowledge, and subsequently integrates these enhanced pLMs with large language models (LLMs) to advance protein understanding. In this framework, we propose a novel instruction tuning pipeline. First, we warm up the enhanced pLMs using contrastive learning and structure denoising. Then, caption-based instructions are used to establish a basic understanding of proteins. Finally, we refine this understanding by employing a mixture of experts (MoEs) to capture more complex properties and functional information with the same number of activated parameters. Moreover, we construct the largest and most comprehensive protein instruction dataset to date, which allows us to train and evaluate the general-purpose protein understanding model. Extensive experiments on both open-ended generation and closed-set answer tasks demonstrate the superior performance of SEPIT over both closed-source general LLMs and open-source LLMs trained with protein knowledge.

Scientific researchers need intensive information about datasets to effectively evaluate and develop theories and methodologies. The information needs regarding datasets are implicitly embedded in particular research tasks, rather than explicitly expressed in search queries. However, existing scientific retrieval and question-answering (QA) datasets typically address straightforward questions, which do not align with the distribution of real-world research inquiries. To bridge this gap, we developed ScIRGen, a dataset generation framework for scientific QA & retrieval that more accurately reflects the information needs of professional science researchers, and uses it to create a large-scale scientific retrieval-augmented generation (RAG) dataset with realistic queries, datasets and papers. Technically, we designed a dataset-oriented information extraction method that leverages academic papers to augment the dataset representation. We then proposed a question generation framework by employing cognitive taxonomy to ensure the quality of synthesized questions. We also design a method to automatically filter synthetic answers based on the perplexity shift of LLMs, which is highly aligned with human judgment of answers' validity. Collectively, these methodologies culminated in the creation of the 61k QA dataset, ScIRGen-Geo. We benchmarked representative methods on the ScIRGen-Geo dataset for their question-answering and retrieval capabilities, finding out that current methods still suffer from reasoning from complex questions. This work advances the development of more sophisticated tools to support the intricate information needs of the scientific community.

As GPUs are widely deployed in High Performance Computing systems, it is critical to ensure that these systems can perform reliably. To improve system reliability, researchers estimate the error resilience of GPU programs by understanding resilience characteristics or modeling error propagation. However, features indicative of resilience rely on manual extraction from simulations of numerous faults, and error propagation analysis cannot target fine-grained bit-level faults. To address those problems, this paper introduces a novel paradigm, namely InstrDGM, for efficiently predicting GPU error resilience. Specifically, InstrDGM first fine-tunes a large language model using extensive sequences of GPU assembly instructions for extracting the semantic representation of instructions automatically. Meanwhile, we consider the propagation of bit-level faults during instruction execution and data transfer processes, and leverage graph neural networks to capture their distinct error propagation patterns. Then, the fault embeddings extracted from these error propagation patterns are integrated for error resilience prediction. Additionally, this paper releases a new dataset for GPU error resilience assessment, containing 1.2 million fault samples. Finally, extensive experiments show that InstrDGM significantly outperforms existing methods.

We focus on unlearning unwanted knowledge in autoregressive large language models (LLMs) through pruning. Our goal is to selectively remove undesirable information (e.g., harmful responses, privacy-sensitive data) while ensuring the preservation of desirable knowledge (e.g., positive responses and objective facts). Previous approaches use gradient ascent (GA) over undesired knowledge to inversely optimize LLMs, which compromises the model's performance on desired knowledge. To address this limitation, we introduce a novel two-stage approach, named LLM-Eraser, for selectively identifying and editing parameters specifically associated with undesirable knowledge. LLM-Eraser operates in two stages: localization and unlearning. During the localization stage, we utilize neuron scores and trainable soft masks to identify parameters crucial to the undesired knowledge. In the unlearning stage, we prune these identified parameters and apply a selective post-training process to enhance the model's selectiveness. Our experiments, conducted across five task datasets, demonstrate that LLM-Eraser effectively unlearns undesirable knowledge-evidenced by the model's near-random performance on multiple-choice questions related to the erased knowledge-while maintaining high proficiency in desirable knowledge, with an average performance deficit of only 2.5%.

Recent years have witnessed the prevalent integration of Large Language Models (LLMs) in various Web applications, such as search engines and recommender systems. As an emerging technique, instruction tuning aims to align pre-trained LLMs as capable chatbots that excel at following human instructions. Previous research indicates that selecting an appropriate subset of a large instruction dataset can enhance the capabilities of LLMs and reduce training costs. However, existing works tend to overlook external correlations between instruction examples during data selection process, which can introduce potential bias and lead to sub-optimal performance. To bridge this gap, we formalize this problem from graph influence maximization perspective and propose Uncertainty-aware influence Maximization (UniMax), a data selection framework that explicitly incorporates the complex inter-dependencies within instruction data. Specifically, we first define a latent instruction graph, treating each instruction example as a graph node and representing their implicit relations as graph edges. Instead of solely relying on heuristic metrics for graph construction, we develop a self-supervised graph learner to uncover the latent structure beyond surface-level feature correlations. After that, we propose an uncertainty-aware influence function to score each example on the instruction graph, allowing a simple greedy algorithm to select a valuable subset that embodies both high influence and uncertainty with an approximation guarantee. Extensive experiments on public datasets show that the proposed approach can significantly enhance model capabilities, underscoring the importance of exploiting data dependencies in instruction data selection.

Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve incorporating auxiliary information or learning strategies from interaction data. However, they struggle with the inherent limitations of external knowledge and interaction data, as well as the non-universality of certain predefined assumptions, hindering accurate noise identification. Recently, large language models (LLMs) have gained attention for their extensive world knowledge and reasoning abilities, yet their potential in enhancing denoising in recommendations remains underexplored. In this paper, we introduce LLaRD, a framework leveraging LLMs to improve denoising in recommender systems, thereby boosting overall recommendation performance. Specifically, LLaRD generates denoising-related knowledge by first enriching semantic insights from observational data via LLMs and inferring user-item preference knowledge. It then employs a novel Chain-of-Thought (CoT) technique over user-item interaction graphs to reveal relation knowledge for denoising. Finally, it applies the Information Bottleneck (IB) principle to align LLM-generated denoising knowledge with recommendation targets, filtering out noise and irrelevant LLM knowledge. Empirical results demonstrate LLaRD's effectiveness in enhancing denoising and recommendation accuracy.

Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a critical topic in both academia and industry. Existing methods typically focus on enhancing multi-modal information utility through adaptive modality fusion to capture the evolving of user preference from user-item interaction sequences. However, most of them overlook the interference caused by redundant interest-irrelevant information contained in rich multi-modal data. Additionally, they primarily rely on implicit temporal information based solely on chronological ordering, neglecting explicit temporal signals that could more effectively represent dynamic user interest over time. To address these limitations, we propose a Hierarchical time-aware Mixture of experts for multi-modal Sequential Recommendation (HM4SR) with a two-level Mixture of Experts (MoE) and a multi-task learning strategy. Specifically, the first MoE, named Interactive MoE, extracts essential user interest-related information from the multi-modal data of each item. Then, the second MoE, termed Temporal MoE, captures user dynamic interests by introducing explicit temporal embeddings from timestamps in modality encoding. To further address data sparsity, we propose three auxiliary supervision tasks: sequence-level category prediction (CP) for item feature understanding, contrastive learning on ID (IDCL) to align sequence context with user interests, and placeholder contrastive learning (PCL) to integrate temporal information with modalities for dynamic interest modeling. Extensive experiments on four public datasets verify the effectiveness of HM4SR compared to several state-of-the-art approaches.

Spatio-Temporal Prediction (STP) is crucial for various smart city applications, such as traffic management and resource allocation. However, training samples can be scarce in data-constrained scenarios, which often degrades the predictive capability of existing deep STP models. Although recent STP foundation models excel in few-shot and zero-shot learning through extensive pre-training on large-scale, multi-domain spatio-temporal data, they often rely on large parameter scale to achieve enhanced performance, resulting in high computational demands that hinder practical deployment. In response, we develop CompactST, an efficient, compact, and versatile pre-trained model for STP in data-scarce settings. Recognizing the complexities posed by large-scale, heterogeneous pre-training datasets, CompactST integrates three specialized components: (1) a mixture-of-normalizers module to address domain and spatial heterogeneity, (2) a multi-scale spatio-temporal mixer that captures diverse patterns from datasets with varying spatio-temporal resolutions, and (3) an adaptive dataset-oriented tuning module that transfers the handling of dataset-specific parameters from pre-training to fine-tuning stage. These tailored designs enable CompactST to maximize generalizability across diverse datasets while maintaining a compact model size ( i.e. , only 300K parameters). To validate its effectiveness, we pre-train CompactST on a substantial corpus of public spatio-temporal datasets spanning over 10 domains and encompassing 300 million data points. Extensive experimental results on ten real-world datasets demonstrate CompactST's significantly improved prediction accuracy and efficiency in data-scarce scenarios.

Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user's skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced Transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability.

Continuously learning new skills is essential for talents to gain a competitive advantage in the labor market. Despite extensive efforts on relevance- or preference-based skill recommendations, little attention has been given to the practical effects of job skills in the market. To bridge this gap, we propose an explainable personalized skill learning recommendation system that considers the long-term learning benefits and costs. Specifically, we model skill learning utilities based on salary and learning cost associated with job positions and propose a multi-objective deep reinforcement learning framework to model and maximize long-term utilities. Furthermore, we propose a Self-explaining Skill Recommendation Deep Q-network (SeSRDQN) that captures and prototypes prevalent skill sets in the market into representative exemplars for decision-making. SeSRDQN quantitatively decomposes the talent’s long-term learning utility into contributions from each exemplar, offering a comprehensive and multi-factorial explanation across various skill learning options. To tackle the combinatorial complexity of the skill space, we develop an MCTS-based optimization-decoding iterative training procedure for explanation fidelity and human understandability. In this way, talents will receive a tailored roadmap of essential skills, complemented by exemplar-based explanations, to effectively plan their careers. Extensive experiments on a real-world dataset validate the effectiveness and explainability of our approach.

Cross-Domain Sequential Recommendation (CDSR) aims to predict users’ preferences based on historical sequential interactions across multiple domains. Existing works focus on the overlapped users who interact in multiple domains to capture the cross-domain correlations. These methods often underperform in practical scenarios featuring both overlapped and non-overlapped users due to the limited cross-domain interactions and knowledge transfer misalignment for non-overlapped users. To address this, we leverage Large Language Models (LLMs) to facilitate CDSR by fully exploiting single-domain interactions. However, LLMs exhibit inherent limitations in handling extensive item repositories and sequential collaborative signals. Moreover, the generation reliability is compromised by the hallucination problem, potentially causing noisy and unstable outputs. To this end, we propose a novel LLMCDSR framework, which employs LLMs to predict unobserved cross-domain interactions, termed pseudo items, within single-domain interactions. Specifically, we first prompt LLMs to execute the Candidate-Free Cross-Domain Interaction Generation task. Then, we devise a Collaborative-Textual Contrastive Pre-Training strategy, learning to infuse collaborative information into textual features. Afterwards, we present a novel Relevance-Aware Meta Recall Network (RMRN) to selectively identify and retrieve high-quality pseudo items from the dataset, where the parameters are optimized in a meta-learning manner. Finally, extensive experiments on two public datasets validate the effectiveness of LLMCDSR in enhancing CDSR. The code and data are available at https://github.com/xhran2010/LLMCDSR .

Tabular data are the most widely used data formats in almost every application domain, such as, biology, ecology, and material science. The purpose of tabular data-centric AI is to use AI to augment the predictive power of tabular data to get better AI. Tabular data-centric AI is essential because it can reconstruct distance measures, reshape discriminative patterns, and improve data AI readiness (structural, predictive, interaction, and expression levels), which is significant in industries and real-world deployments. Therefore, our tutorial is designed to capture the interest of professionals with expertise in artificial intelligence, machine learning, and data mining, as well as researchers engaged in specific application areas and interdisciplinary studies. Examples of such applications include quality control, predictive maintenance, supply chain optimization, process efficiency improvements, biomarker identification, material performance screening. In this tutorial, we will explore the emerging field of Tabular Data-Centric AI. Our discussion will provide a comprehensive overview of this domain: (1) We will demonstrate the different settings within this research domain based on distinct application scenarios. (2) We will identify and explain the significant challenges encountered in tabular data-centric AI. (3) We will highlight existing methods and benchmarks. (4) We will discuss future potential directions for this domain and examine its interconnections with other research areas. To enhance the learning experience, this tutorial will include a hands-on section designed to teach participants the fundamental aspects of developing, evaluating and visualizing techniques in tabular data-centric AI. After this tutorial, attendees will have a deep understanding of tabular data-centric AI research, including its key challenges, seminal techniques, and insights into integrating tabular data-centric AI into their own research.

Traffic Signal Control(TSC), a pivotal and challenging research area in the transportation domain, aims to alleviate congestion at urban intersections by optimizing vehicular flows from different inflow directions. While large efforts have been focused on using Reinforcement Learning(RL) based methods to tackle the TSC problem, it possesses constraints such as unpredictable training duration and risks of online exploration, limiting its real-world deployment. Recently, offline RL has emerged as a new solution by transitioning from learning through online interactions to deriving policies from pre-collected datasets, which guarantees a safer and more efficient learning process. However, existing offline methods overlook the crucial temporal and spatial intricacy among data from different traffic signals at different timesteps, which leads to suboptimal performance. To this end, in this paper, we present an innovative formulation of the offline TSC problem by introducing a spatio-temporal graph to model the historical Markov Decision Process sequences across all traffic signals within the road network. Along this line, we propose STLight, a novel spatio-temporal sequence modeling approach to predict optimal actions for the signals from historical data, accounting for the inherent inter-dependencies among them. Specifically, we incorporate a spatio-temporal encoder to represent states, actions, and returns by capturing dynamic and spatially dependent information. The ordered space-time-aware representations are further fed to the Action Decoder to predict signal phase actions in an auto-regressive manner, accounting for the hidden dependencies between the actions and the reward and state tokens. Furthermore, to adaptively handle tasks with different levels of congestion scenarios, we incorporate space-aware return-based contrastive learning to automatically differentiate data samples with disparate traffic flow patterns. Finally, extensive experiments conducted on two public real-world traffic datasets clearly demonstrate the superior performance of the proposed model over both the state-of-the-art online and offline traffic signal control baselines.

Data scale has been acknowledged as a crucial factor for enhancing the generalization and effectiveness of pre-training models. While existing methods of multivariate time series pre-training are primarily limited to a single specific dataset, scaling to a larger scenario that includes multiple diverse datasets (e.g., multi-region data) remains a substantial challenge. In this paper, we present a novel Decoupled Spatial-Temporal Representation Learning (DeSTR) framework to serve as the backbone network for investigating the data scaling capability of multivariate time series pre-training architectures. Specifically, DeSTR utilizes two separate encoders to capture both the temporal dynamics within each time series and the spatial correlations among multiple variables. The obtained representations of distinct modalities are then fed into a Spatial-Guided Temporal Transformer to equip the temporal features with spatial discriminative information. Moreover, we employ masked autoencoding as the foundational pre-training framework and introduce spacetime-agnostic augmentation to improve robustness and facilitate implicit spatiotemporal modeling. Finally, we successfully pre-train a unified time series representation learning framework on real-world datasets from three different cities. Extensive experiments are carried out on various downstream tasks to validate the performance of DeSTR, compared with three categories of state-of-the-art baselines: deep sequential models, spatial-temporal graph neural networks, and time series representation learning methods. The results clearly demonstrate the advantages of scaling multivariate time series pre-training to multiple datasets, highlighting the effectiveness of DeSTR as a general spatiotemporal learner.

As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors.

Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments. The recent emergence of foundation models such as ChatGPT marks a revolutionary shift in the fields of machine learning and artificial intelligence. Their unparalleled capabilities in contextual understanding, problem solving, and adaptability across a wide range of tasks suggest that integrating these models into urban domains could have a transformative impact on the development of smart cities. Despite growing interest in Urban Foundation Models (UFMs), this burgeoning field faces challenges such as a lack of clear definitions and systematic reviews. To this end, this paper first introduces the concept of UFMs and discusses the unique challenges involved in building them. We then propose a data-centric taxonomy that categorizes and clarifies current UFM-related works, based on urban data modalities and types. Furthermore, we explore the application landscape of UFMs, detailing their potential impact in various urban contexts. Relevant papers and open-source resources have been collated and are continuously updated at: https://github.com/usail-hkust/Awesome-Urban-Foundation-Models.

Accurate traffic forecasting is crucial for the development of Intelligent Transportation Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional forecasting methods, however, struggle with the irregular traffic time series resulting from adaptive traffic signal controls, presenting challenges in asynchronous spatial dependency, irregular temporal dependency, and predicting variable-length sequences. To this end, we propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) tailored for irregular traffic time series forecasting. Specifically, we first propose an Asynchronous Graph Diffusion Network to capture the spatial dependency between asynchronously measured traffic states regulated by adaptive traffic signals. After that, to capture the temporal dependency within irregular traffic state sequences, a personalized time encoding is devised to embed the continuous time signals. Then, we propose a Transformable Time-aware Convolution Network, which adapts meta-filters for time-aware convolution on the sequences with inconsistent temporal flow. Additionally, a Semi-Autoregressive Prediction Network, comprising a state evolution unit and a semiautoregressive predictor, is designed to predict variable-length traffic sequences effectively and efficiently. Extensive experiments on a newly established benchmark demonstrate the superiority of ASeer compared with twelve competitive baselines across six metrics.

Rapid urbanization has significantly escalated traffic congestion, underscoring the need for advanced congestion prediction services to bolster intelligent transportation systems.As one of the world's largest ride-hailing platforms, DiDi places great emphasis on the accuracy of congestion prediction to enhance the effectiveness and reliability of their real-time services, such as travel time estimation and route planning.Despite numerous efforts have been made on congestion prediction, most of them fall short in handling heterogeneous and dynamic spatio-temporal dependencies (e.g., periodic and non-periodic congestions), particularly in the presence of noisy and incomplete traffic data.In this paper, we introduce a Congestion Prediction Mixture-of-Experts, CP-MoE, to address the above challenges.We first propose a sparsely-gated Mixture of Adaptive Graph Learners (MAGLs) with congestion-aware inductive biases to improve the model capacity for efficiently capturing complex spatio-temporal dependencies in varying traffic scenarios.Then, we devise two specialized experts to help identify stable trends and periodic patterns within the traffic data, respectively.By cascading these experts with MAGLs, CP-MoE delivers congestion predictions in a more robust and interpretable manner.Furthermore, an ordinal regression strategy is adopted to facilitate effective * Corresponding author.

In recent years, Graph Neural Networks (GNNs) and Large Language Models (LLMs) have exhibited remarkable capability in addressing different graph learning and natural language tasks, respectively. Motivated by this, integrating LLMs with GNNs has been increasingly studied to acquire transferable knowledge across modalities, which leads to improved empirical performance in language and graph domains. However, existing studies mainly focused on a single-domain scenario by designing complicated integration techniques to manage multimodal data effectively. Therefore, a concise and generic learning framework for multi-domain tasks, i.e., graph and language domains, is highly desired yet remains under-exploited due to two major challenges. First, the language corpus of downstream tasks differs significantly from graph data, making it hard to bridge the knowledge gap between modalities. Second, not all knowledge demonstrates immediate benefits for downstream tasks, potentially introducing disruptive noise to context-sensitive models like LLMs. To tackle these challenges, we propose a novel plug-and-play framework for incorporating a lightweight cross-domain prompting method into both language and graph learning tasks. Specifically, we first convert the textual input into a domain-scalable prompt, which not only preserves the semantic and logical contents of the textual input, but also highlights related graph information as external knowledge for different domains. Then, we develop a reinforcement learning-based method to learn the optimal edge selection strategy for useful knowledge extraction, which profoundly sharpens the multi-domain model capabilities. In addition, we introduce a joint multi-view optimization module to regularize agent-level collaborative learning across two domains. Finally, extensive empirical justifications over 23 public and synthetic datasets demonstrate that our approach can be applied to diverse multi-domain tasks more accurately, robustly, and reasonably, and improve the performances of the state-of-the-art graph and language models in different learning paradigms.

The recent advancements in Traffic Signal Control (TSC) have highlighted the potential of Reinforcement Learning (RL) as a promising solution to alleviate traffic congestion. Current research in this area primarily concentrates on either online or offline learning strategies, aiming to create optimized policies for specific cities. Nevertheless, the transferability of these policies to new cities is impeded by constraints such as the limited availability of high-quality data and the expensive and risky exploration process. To this end, in this paper, we present an innovative cross-city Traffic Signal Control (TSC) paradigm called CrossLight. Our approach involves meta training using offline data from source cities and adaptively fine-tuning in the target city. This novel methodology aims to address the challenges of transferring TSC policies across different cities effectively. In our proposed approach, we start by acquiring meta-decision pattern knowledge through trajectory dynamics reconstruction via pre-training in source cities. To address disparities in road network topologies between cities, we dynamically construct city topological structures based on the extracted meta-knowledge during the offline meta-training phase. These structures are then used to distill pattern-structure aware representations of decision trajectories from the source cities. To identify effective initial parameters for the learnable components, we employ the Model-Agnostic Meta-Learning (MAML) framework, a popular meta-learning approach. During adaptive fine-tuning in the target city, we introduce a replay buffer that is iteratively updated using online interactions with a rank and filter mechanism. This mechanism, along with a carefully designed exploration strategy, ensures a balance between exploitation and exploration, thereby fostering both the diversity and quality of the trajectories for fine-tuning. Finally, extensive experiments across four cities validate that CrossLight achieves comparable performance in new cities with minimal fine-tuning iterations, surpassing both existing online and offline methods. This success underscores that our CrossLight framework emerges as a groundbreaking and potent paradigm, offering a feasible and effective solution to the intelligent transportation community.

Understanding urban regional characteristics is pivotal in driving critical insights for urban planning and management. We have witnessed the successful application of pre-trained Foundation Models (FMs) in generating universal representations for various downstream tasks. However, applying this principle to the geospatial domain remains challenging, primarily due to the difficulty of gathering extensive data for developing a dedicated urban foundation model. Though there have been some attempts to empower the existing FMs with urban data, most of them focus on single-modality FMs without considering the multi-modality nature of urban region understanding tasks. To address this gap, we introduce ReFound - a novel framework for Re-training a Foundation model for urban region understanding, harnessing the strengths of both language and visual FMs. In this framework, we first invent a Mixture-of-Geospatial-Expert (MoGE) Transformer, to effectively integrate the embedding of multi-source geospatial data. Building on this, ReFound is enhanced by jointly distilling knowledge from language, visual, and visual-language FMs respectively, thus augmenting its generalization capabilities. Meanwhile, we design a masked geospatial data modeling approach alongside a cross-modal spatial alignment mechanism, to enhance the spatial knowledge of ReFound derived from geospatial data. Extensive experiments conducted on six real-world datasets over three urban region understanding tasks demonstrate the superior performance of our framework.

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.

In recent years, Optimized Cost Per Click (OCPC) and Optimized Cost Per Mille (OCPM) have emerged as the most widely adopted pricing models in the online advertising industry. However, the existing literature has yet to identify the specific conditions under which these models outperform traditional pricing models like Cost Per Click (CPC) and Cost Per Action (CPA). To fill the gap, this paper builds an economic model that compares OCPC with CPC and CPA theoretically, which incorporates out-site scenarios and outside options as two key factors. Our analysis reveals that OCPC can effectively replace CPA by tackling the problem of advertisers strategically manipulating conversion reporting in out-site scenarios where conversions occur outside the advertising platform. Furthermore, OCPC exhibits the potential to surpass CPC in platform payoffs by providing higher advertiser payoffs and consequently attracting more advertisers. However, if advertisers have less competitive outside options and consistently stay in the focal platform, the platform may achieve higher payoffs using CPC. Our findings deliver valuable insights for online advertising platforms in selecting optimal pricing models, and provide recommendations for further enhancing their payoffs. To the best of our knowledge, this is the first study to analyze OCPC from an economic perspective. Moreover, our analysis can be applied to the OCPM model as well.

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with talent and management-related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision-making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 5th International Workshop on Talent and Management Computing (TMC'2024), is to bring together researchers and practitioners to discuss both the critical problems faced by talent and management-related domains and potential data-driven solutions by leveraging state-of-the-art data mining technologies.

Text classification has long time been researched as a fundamental problem in information retrieval. Since text data are frequently connected with graph structures, it poses new possibilities for a more accurate and explainable classification. One common approach of this graph-text integration is to consider text as graph attributes and utilize GNNs to conduct a node classification task. While both text and graph data are modeled, GNNs treat text in a rather coarse-grained way, have limitations in preserving the detailed structures of a graph, and are less robust to graph sparsity. In this paper, we propose to take an alternative perspective instead, viewing graph as the context of texts, as enlightened by retrieval augmented generation. We propose a novel framework called Graph Retrieval Prompt Tuning (GRPT), consisting of a Graph Retrieval Module and a Prompt Tuning Module integrated with graph context. For graph retrieval, two retrieval strategies are designed to retrieve node context and path context, preserving both node proximity and detailed connectivity patterns. Extensive experiments on four real-world datasets show the effectiveness of our framework in both standard supervised and sparse settings.

Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal of providing personalized recommendations. While existing research predominantly ascribes this flaw to the over-smoothing problem, this paper underscores the critical, often overlooked role of the over-correlation issue in diminishing the effectiveness of GNN representations and subsequent recommendation performance. Up to now, the over-correlation issue remains unexplored in RS. Meanwhile, how to mitigate the impact of over-correlation while preserving collaborative filtering signals is a significant challenge. To this end, this paper aims to address the aforementioned gap by undertaking a comprehensive study of the over-correlation issue in graph collaborative filtering models. Firstly, we present empirical evidence to demonstrate the widespread prevalence of over-correlation in these models. Subsequently, we dive into a theoretical analysis which establishes a pivotal connection between the over-correlation and over-smoothing issues. Leveraging these insights, we introduce the Adaptive Feature De-correlation Graph Collaborative Filtering (AFDGCF) framework, which dynamically applies correlation penalties to the feature dimensions of the representation matrix, effectively alleviating both over-correlation and over-smoothing issues. The efficacy of the proposed framework is corroborated through extensive experiments conducted with four representative graph collaborative filtering models across four publicly available datasets. Our results show the superiority of AFDGCF in enhancing the performance landscape of graph collaborative filtering models.

Reciprocal Recommender Systems (RRSs) which aim to satisfy the preferences of both service providers and seekers simultaneously has attracted significant research interest in recent years. Existing studies on RRSs mainly focus on modeling the bilateral interactions between the users on both sides to capture the user preferences. However, due to the presence of exposure bias, modeling user preferences solely based on bilateral interactions often lacks precision. Additionally, in RRSs, users may exhibit varying preferences when acting in different roles, and how to effectively model users from multiple perspectives remains a substantial problem. To solve the above challenges, in this paper, we propose a novel MultI-view Reciprocal Recommender system for Online Recruitment (MIRROR). Specifically, we first propose to model the users from three different views, respectively search, active, and passive views, and we further design several Transformer-based sequential models to capture the user representation corresponding to each view. Then, we propose to divide the bilateral matching process into three stages, respectively apply, reply, and match, and a multi-stage output layer is designed based on the above multi-view modeling results. To train our MIRROR model, we first design a multi-task learning loss based on the multi-stage output results. Moreover, to bridge the semantic gap between search queries and user behaviors, we additionally design a supplementary task for next-query prediction. Finally, we conduct both offline experiments on five real-world datasets and online A/B tests, and the experiment results clearly validate the effectiveness of our MIRROR model compared with several state-of-the-art baseline methods.

Collaborative filtering is a critical technique in recommender systems. It has been increasingly viewed as a conditional generative task for user feedback data, where newly developed diffusion model shows great potential. However, existing studies on diffusion model lack effective solutions for modeling implicit feedback. Particularly, the standard isotropic diffusion process overlooks correlation between items, misaligned with the graphical structure of the interaction space. Meanwhile, Gaussian noise destroys personalized information in a user's interaction vector, causing difficulty in its reconstruction. In this paper, we adapt standard diffusion model and propose a novel Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF). To better represent the correlated distribution of user-item interactions, we define a generalized diffusion process using heat equation on the item-item similarity graph. Our forward process smooths interaction signals with an advanced family of graph filters, introducing the graph adjacency as beneficial prior knowledge for recommendation. Our reverse process iteratively refines and sharpens latent signals in a noise-free manner, where the updates are conditioned on the user's history and computed from a carefully designed two-stage denoiser, leading to high-quality reconstruction. Finally, through extensive experiments, we show that GiffCF effectively leverages the advantages of both diffusion model and graph signal processing, and achieves state-of-the-art performance on three benchmark datasets.

In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment. This challenge is primarily twofold: reducing the model size while effectively learning user and item representations for efficient recommendations. Despite considerable advancements in model compression and architecture search, prevalent approaches face notable constraints. These include substantial additional computational costs from pre-training/re-training in model compression and an extensive search space in architecture design. Additionally, managing complexity and adhering to memory constraints is problematic, especially in scenarios with strict time or space limitations. Addressing these issues, this paper introduces a novel learning paradigm, Dynamic Sparse Learning (DSL), tailored for recommendation models. DSL innovatively trains a lightweight sparse model from scratch, periodically evaluating and dynamically adjusting each weight's significance and the model's sparsity distribution during the training. This approach ensures a consistent and minimal parameter budget throughout the full learning lifecycle, paving the way for "end-to-end" efficiency from training to inference. Our extensive experimental results underline DSL's effectiveness, significantly reducing training and inference costs while delivering comparable recommendation performance. We give an code link of our work: https://github.com/shuyao-wang/DSL.

In recent years, the rise of online Knowledge Management Systems (KMSs) has significantly improved work efficiency in enterprises. Knowledge development prediction, as a critical application within these online platforms, enables organizations to proactively address knowledge gaps and align their learning initiatives with evolving job requirements. However, it still confronts challenges in exploring the influence of collaborative networks on knowledge development and adapting to ecological situations in working environment. To this end, in this paper, we propose a Collaboration-Aware Hybrid Learning approach (CAHL) for predicting the future knowledge acquisition of employees and quantifying the impact of various knowledge learning patterns. Specifically, to fully harness the inherent rules of knowledge development, we first learn the knowledge co-occurrence and prerequisite relationships with an association prompt attention mechanism to generate effective knowledge representations through a specially-designed Job Knowledge Embedding module. Then, we aggregate the features of mastering knowledge and work collaborators for employee representations in another Employee Embedding module. Moreover, we propose to model the process of employee knowledge development via a Hybrid Learning Simulation module that integrates both collaborative learning and self learning to predict future-acquired job knowledge of employees. Finally, extensive experiments conducted on a real-world dataset clearly validate the effectiveness of CAHL.

Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of recommender systems. Due to its rich semantic content and associations among interactive entities, it can effectively alleviate inherent limitations in collaborative filtering (CF), such as data sparsity or cold-start issues. However, most existing knowledge-aware recommendation models indiscriminately aggregate all information in KG, without considering information specifically relevant to the recommendation task. Such indiscriminate aggregation could introduce additional noisy knowledge into representation learning, which can distort the understanding of users' genuine preferences, thereby sacrificing the recommendation quality. In this paper, we introduce the principle of invariance to the knowledge-aware recommendation, culminating in our Knowledge Graph Invariant Learning (KGIL) framework. It aims to discern and harness the task-relevant knowledge connections within KG to enhance the recommendation models. Specifically, we employ multiple environment generators to simulate diverse noisy KG-environments. Then we devise a novel attention learning mechanism for KG and user-item interaction graph, aiming to learn environment-invariant subgraphs. Leveraging an adversarial optimization strategy, we enhance the diversity of the environments, meanwhile, promote invariant representation learning across environments. We conduct extensive experiments on three datasets and compare KGIL with state-of-the-art methods. The experimental results further demonstrate the superiority of our approach.

Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textual information, such as product descriptions in online shopping or news headlines on social media, LLMs require longer texts to comprehensively depict the historical user behavior sequence. This poses significant challenges to LLM-based recommenders, such as over-length limitations, extensive time and space overheads, and suboptimal model performance. To this end, in this paper, we design a novel framework for harnessing Large Language Models for Text-Rich Sequential Recommendation (LLM-TRSR). Specifically, we first propose to segment the user historical behaviors and subsequently employ an LLM-based summarizer for summarizing these user behavior blocks. Particularly, drawing inspiration from the successful application of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) models in user modeling, we introduce two unique summarization techniques in this paper, respectively hierarchical summarization and recurrent summarization. Then, we construct a prompt text encompassing the user preference summary, recent user interactions, and candidate item information into an LLM-based recommender, which is subsequently fine-tuned using Supervised Fine-Tuning (SFT) techniques to yield our final recommendation model. We also use Low-Rank Adaptation (LoRA) for Parameter-Efficient Fine-Tuning (PEFT). We conduct experiments on two public datasets, and the results clearly demonstrate the effectiveness of our approach.

Spatio-Temporal Graph Neural Network (STGNN) has been used as a common workhorse for traffic forecasting. However, most of them require prohibitive quadratic computational complexity to capture long-range spatio-temporal dependencies, thus hindering their applications to long historical sequences on large-scale road networks in the real-world. To this end, in this paper, we propose BigST, a linear complexity spatio-temporal graph neural network, to efficiently exploit long-range spatio-temporal dependencies for large-scale traffic forecasting. Specifically, we first propose a scalable long sequence feature extractor to encode node-wise long-range inputs ( e.g. , thousands of time-steps in the past week) into low-dimensional representations encompassing rich temporal dynamics. The resulting representations can be pre-computed and hence significantly reduce the computational overhead for prediction. Then, we build a linearized global spatial convolution network to adaptively distill time-varying graph structures, which enables fast runtime message passing along spatial dimensions in linear complexity. We empirically evaluate our model on two large-scale real-world traffic datasets. Extensive experiments demonstrate that BigST can scale to road networks with up to one hundred thousand nodes, while significantly improving prediction accuracy and efficiency compared to state-of-the-art traffic forecasting models.

Wind is one kind of high-efficient, environmentally-friendly, and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a more and more important role in supplying electricity. Though growing dramatically in recent years, the amount of generated wind power can be directly or latently affected by multiple uncertain factors, such as wind speed, wind direction, temperatures, and so on. More importantly, there exist very complicated dependencies of the generated power on the latent composition of these multiple time-evolving variables, which are always ignored by existing works and thus largely hinder the prediction performances. To this end, we propose DEWP , a novel D eep E xpansion learning for W ind P ower forecasting framework to carefully model the complicated dependencies with adequate expressiveness. DEWP starts with a stack-by-stack architecture, where each stack is composed of (i) a variable expansion block that makes use of convolutional layers to capture dependencies among multiple variables; (ii) a time expansion block that applies Fourier series and backcast/forecast mechanism to learn temporal dependencies in sequential patterns. These two tailored blocks expand raw inputs into different latent feature spaces which can model different levels of dependencies of time-evolving sequential data. Moreover, we propose an inference block corresponding for each stack, which applies multi-head self-attentions to acquire attentive features and maps expanded latent representations into generated wind power. In addition, to make DEWP more expressive in handling deep neural architectures, we adapt doubly residue learning to process stack-by-stack outputs. Accurate wind power forecasting (WPF) is then better achieved through fine-grained outputs by continuously removing stack residues and accumulating useful stack forecasts. Finally, we present extensive experiments in the real-world WPF application on two datasets from two different turbines, in order to demonstrate the effectiveness of our approach.

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses. These provide empirical evidence of the strides made in this journal version over the initial work, solidifying our framework’s standing as a substantial contribution to the field of automated feature transformation. To improve the reproducibility, we have released the associated code and data by the Github link https://github.com/coco11563/TKDD2023_code.

Minimizing privacy leakage while ensuring data utility is a critical problem in a privacy-preserving data publishing task, from which data holders can boost platform engagements or enlarge data values. Most prior research concerned only with either privacy-insensitive or exact private data and resorts to a single obscuring method to achieve a privacy-utility tradeoff, which is inadequate for real-life hybrid data especially when facing machine learning-based inference attacks. This work takes a pilot study on privacy-preserving data publishing when both widely adopted generalization and obfuscation operations are employed for privacy-heterogeneous data protection. Specifically, we first propose novel measures for privacy and utility values quantification and formulate the hybrid privacy-preserving data obscuring problem to account for the joint effect of generalization and obfuscation. We then design a novel protection mechanism called HyObscure, which decomposes the original problem into three sub-problems to cross-iteratively optimize the hybrid operations for maximum privacy protection under a certain data utility guarantee. The convergence of the iterative process and the privacy leakage bound of HyObscure are also provided in theory. Extensive experiments demonstrate that HyObscure significantly outperforms a variety of state-of-the-art baseline methods when facing various inference attacks in different scenarios.

Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. However, existing solutions usually treat protein-ligand complexes as topological graph data, thus the 3D geometry-based biomolecular structural information is not fully utilized. The essential intermolecular interactions with long-range dependencies, including type-wise interactions and molecule-wise interactions, are also neglected in GNN models. To this end, we propose a geometry-aware interactive graph neural network (GIaNt) which consists of two components: 3D geometric graph learning network (3DG-Net) and pairwise interactive learning network (Pi-Net). Specifically,3DG-Netiteratively performs the node-edge interaction process to update embeddings of nodes and edges in a unified framework while preserving the 3D geometric factors among atoms, including spatial distance, polar angle and dihedral angle information in 3D space. Moreover,Pi-Netis adopted to incorporate both element type-level and molecule-level interactions. Specially, interactive edges are gathered with a subsequent reconstruction loss to reflect the global type-level interactions. Meanwhile, a pairwise attentive pooling scheme is designed to identify the critical interactive atoms for complex representation learning from a semantic view. An exhaustive experimental study on two benchmarks verifies the superiority ofGIaNt.

Numerous spatial-temporal learning methods have been proposed for crowd flow modeling, which is an important problem in Intelligent Transportation Systems (ITS). However, most of the existing methods were designed to use data in one specific form to solve one particular task of crowd flow modeling and the shared patterns among different tasks have been largely ignored. In this paper, we investigate how to learn generic node representations that can simultaneously support various downstream tasks of crowd flow modeling. Along this line, we develop a continuous-time dynamic graph representation learning method based onBootstrapping forCrowdFlow modeling (BootCF). Our approach follows a training procedure with two phases. In the pre-training phase, the continuous-time dynamic encoder converts edges with timestamps into messages to update the representations of the related traffic nodes. Inspired by the recent progress of contrastive learning, a bootstrapping framework for continuous-time dynamic graphs is designed to calculate pre-training loss and update the model in a self-supervised way, and thus enabling the node representation learning to be task-agnostic. Moreover, a context-aware data augmentation on continuous-time dynamic graphs is proposed to generate the augmented view of input data. Once the general node representations are obtained, the second phase can learn an effective model for any downstream task. Experiments on two real-world datasets show that our approach can achieve significant performance gain on four downstream tasks, which demonstrates that the proposed method has the powerful generalization capability for learning task-agnostic node representations.

Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them bored and unsatisfied. The key to solving these issues is learning users' fine-grained preferences and recommending appealing and unexplored items deviating from users' historical items. However, existing models consider cold-start or filter bubble problems separately and ignore that they can reinforce mutually and damage the models' performance accuracy. To this end, we devise a novel serendipity-oriented recommender system (GenerativeSelf-constrainedSerendipitousRecommenderSystem,GS$^{2}$-RS) that generates users' fine-grained preferences to enhance the recommendation performance. Specifically, GS$^{2}$-RS extracts users' interest and satisfaction preferences and generates virtual but convincible neighbors' preferences from themselves with a twin Conditional Generative Adversarial Nets (not from real neighbors). Then we introduce the serendipity item, which is low-interest but high-satisfaction among candidate items. We use the serendipity item to improve the diversity of recommended items, which relieves the filter-bubble problem. Along with this line, a gated mechanism is applied to their fine-grained preferences (interests, satisfactions) to obtain their serendipity items. Finally, these serendipity items are inversely injected into the original user-item rating matrix and build a relatively dense matrix as the input for backbone RS models. Note that GS$^{2}$-RS tackles cold-start and filter-bubble problems in a unified framework without any additional side information and enriches the interpretability of recommendation models. We comprehensively validate GS$^{2}$-RS for solving cold-start and filter bubble problems on four real-world benchmark datasets. Extensive experiments illustrate GS$^{2}$-RS's superiority in accuracy, serendipity, and interpretability over state-of-the-art models. Also, we can plug our model into existing recommender systems as a preprocessing procedure to enhance their performance.

Next point-of-interest (POI) recommendation has been a hot research topic, which enables new paradigms for kinds of location-based services in real-world scenarios. Due to the privacy concerns and rigorous data regulations, federated learning provides a distributed learning framework to collaboratively train the recommendation model without sharing the highly sensitive POI data with others. However, there exist two main challenges, namelylocation noise, andbalance between personalization and knowledge sharing, seriously restrict the development of the federated next POI recommendation. To this end, in this work, we propose an adaptive clustering based personalized federated learning framework for next POI recommendation with location noise, namedCPF-POI, to address the above challenges. In detail, within the local client, a location recovery module can efficiently remove noises under the given assumption from the noisy POI data in which the recovery error bound can be theoretically proved. Then, within the parameter server, an adaptive clustering scheme is proposed to capture the internal relatedness among all clients to augment positive knowledge sharing. In order to make a balance between personalization and knowledge sharing under personalized federated learning framework, we design an alternative optimization process between clustering similar clients and minimizing local personalized loss functions. Finally, extensive experiments are conducted on two diverse real-world datasets to show the advantages ofCPF-POIover state-of-the-art methods. improvement across all metrics on average.

It is always challenging task for students to select right universities. For students, graduate job placement is the most important component of university quality. However, existing university evaluation methods predominantly depend on either subjective criteria, such as the perceived quality of the learning environment and academic prestige, or on factors like faculty excellence, which may not provide a precise indication of graduate job placement. Indeed, there is still a lack of a data-driven approach to accurately measure university quality based on the employment situation of graduates. Moreover, the inherently unsupervised nature of university evaluation, compounded by the absence of a reasonable ground truth, necessitates the development of a reliable supervised methodology to precisely quantify university quality. Our basic assumption is that highly influential companies would attract graduates from high-ranking universities. To this end, in this paper, we formulate university evaluation problem into the graduate flow prediction problem, and propose an Influence based Graph Autoencoder (IGAE) method to learn the representation of universities based on the employment of their graduates. Specifically, we first build a talent transition graph based on the massive resume information. This graph reveals the flow of talent between institutions. Then, considering the asymmetric and heterogeneous properties of talent flow, an unidirectional aggregation process with a heterogeneous attention mechanism is designed to encode the nodes in the directed graph and preserve the influence terms at the same time. Afterwards, a novel dual self-attention module is exploited to capture the dynamic pattern of institutions to forecast future employment. Furthermore, we design an influence based decoder to predict the existence of talent flows and estimate the frequency of employment, which can be learnt in a joint learning framework. Finally, we conduct extensive experiments on a real-world dataset for performance evaluation. The experimental results clearly validate the effectiveness of our approach compared to the state-of-the-art baselines, and we provide a case study on university influence analysis.

Cognitive diagnosis has been widely recognized as a crucial task in the field of computational education, which is capable of learning the knowledge profiles of students and predicting their future exercise performance. Indeed, considerable research efforts have been made in this direction over the past decades. However, most of the existing studies only focus on individual-level diagnostic modeling, while the group-level cognitive diagnosis still lacks an in-depth exploration, which is more compatible with realistic collaborative learning environments. To this end, in this paper, we propose aRelation-guidedDual-sideGraphTransformer (RDGT) model for achieving effective group-level cognitive diagnosis. Specifically, we first construct the dual-side relation graphs (i.e., student-side and exercise-side) from the group-student-exercise heterogeneous interaction data for explicitly modeling associations between students and exercises, respectively. In particular, the edge weight between two nodes is defined based on the similarity of corresponding student-exercise interactions. Then, we introduce two relation-guided graph transformers to learn the representations of students and exercises by integrating the whole graph information, including both nodes and edge weights. Meanwhile, the inter-group information has been incorporated into the student-side relation graph to further enhance the representations of students. Along this line, we design a cognitive diagnosis module for learning the groups' proficiency in specific knowledge concepts, which includes an attention-based aggregation strategy to obtain the final group representation and a hybrid loss for optimizing the performance prediction of both group and student. Finally, extensive experiments on 5 real-world datasets clearly demonstrate the effectiveness of our model as well as some interesting findings (e.g., the representative groups and potential collaborations among students).

Recent years have witnessed the fast development of deep learning techniques in quantitative trading. It still remains unclear how to exploit deep learning techniques to improve high-frequency trading (HFT). Indeed, there are two emerging challenges for the use of deep learning for HFT: (i) how to quantify fast-changing market conditions for tick-level signal prediction; (ii) how to establish a unified trading paradigm for different securities of diverse market conditions and severe signal sparsity. To this end, in this paper, we propose anOnlineAdaptive Sequence Learning(LEVER) framework, which consists of two distinct components to predict the HFT signals at the tick level for a variety of securities simultaneously. Specifically, we start with a single learner that adopts an encoder-decoder architecture for each security-based HFT signal prediction. In this single learner, an ordered encoder module first captures the variability patterns of the security's price curve by encoding the input indicator sequence from different time ranges. An unordered decoder module then outlines the pivot points of the price curve as support and resistance levels to quantify the market status. Based on the measured market condition, a prediction module further approximates the impacts of upcoming security data as the potential market momentum to detect the tick-level trading signals. To overcome the computational challenges and signal sparsity posed by online HFT for multiple securities, we develop a competitive active-meta learning paradigm to enhance the signal learners’ learning efficiency for online implementation. Finally, extensive experiments on real-world stock market data demonstrate the effectiveness of our deployed LEVER for improving the performances of the existing industry method by 0.27 in the Sharpe ratio and by 0.09% in a transaction-based return.

The robust multivariate time series anomaly detection can facilitate intelligent decisions and timely maintenance in various kinds of monitor systems. However, the robustness is highly restricted by the stochasticity in multivariate time series, which is summarized astemporal stochasticityandspatial stochasticityspecifically. In this paper, we explicitly model the temporal stochasticity variables and the latent graph relationship variables into a unified graphical framework, which can achieve better robustness to dynamicity from both the spatial and temporal perspective. First, within the spatial encoder, every connection exists or not is modeled as a binary stochastic variable, and the graph structure can be learnt automatically. Then, the temporal encoder would embed the highly structured time series into latent stochastic variables to capture both complex temporal dependencies and neighbors information. Moreover, we design a history-future combined anomaly score mechanism with both reconstruction decoder and forecasting decoder to improve the anomaly detection performance. By weighting the historical anomaly factor, the future anomaly factor, and the prediction error of current timestamp, the anomaly detection at current timestamp could be more sensitive to anomaly detection. Finally, extensive experiments on three publicly available anomaly detection datasets demonstrate our proposed method can achieve the best performance in terms of recall and F1 compared with state-of-the-arts baselines.

Recent years have witnessed the rapid development of online recruitment platforms, which provide a convenient way for matching job seekers and recruiters by leveraging recommendation systems. Indeed, this is areciprocal recommendationproblem which needs to consider the preferences of both job seekers and recruiters simultaneously, making it different from traditional uni-directional user-item recommendation problems. Existing studies mainly focus on building recommendation models based on the matched person-job pairs via text matching or collaborative filtering methods. However, we propose that these methods are limited and insufficient for user modeling in recruitment platforms, since the abundant multi-typed bilateral behaviors (e.g.,apply for conversationandneglect the candidates) among users have been largely ignored. Therefore, in this paper, we propose a novel BilAteral Multi-BehaviOr mOdeling (BAMBOO) method for reciprocal recommendation in online recruitment, which can model the multi-typed interactions between job seekers and recruiters from two different perspectives, respectivelyexpectation perspectiveandcompetitiveness perspective. Specifically, for the expectation perspective, we propose to format the historical behaviors of different users as bilateral multi-behavior sequences, and we utilize a transformer-based model to learn the representations of what the userswantto obtain. For the competitiveness perspective, we propose to construct a bilateral interaction heterogeneous graph to describe the entire recruitment market, and further utilize a heterogeneous graph transformer-based model to learn the representations of what the userscanobtain. Moreover, we utilize contrastive learning methods to enhance these two modules. Furthermore, we propose to decompose the matching probability between job seekers and recruiters into the product of two parts, respectively the probability of the active party initiating the conversation and the probability of the passive party accepting it, and we train our model based on a multi-task learning strategy. Finally, we conduct both offline experiments on real-world datasets and online A/B test, and the experiment results validate the effectiveness of our BAMBOO model compared with several state-of-the-art baseline methods.

In this work, we present TriMLP as a foundational MLP-like architecture for the sequential recommendation, simultaneously achieving computational efficiency and promising performance. First, we empirically study the incompatibility between existing purely MLP-based models and sequential recommendation, that the inherent fully-connective structure endows historical user–item interactions (referred as tokens) with unrestricted communications and overlooks the essential chronological order in sequences. Then, we propose the MLP-based Triangular Mixer to establish ordered contact among tokens and excavate the primary sequential modeling capability under the standard auto-regressive training fashion. It contains (1) a global mixing layer that drops the lower-triangle neurons in MLP to block the anti-chronological connections from future tokens and (2) a local mixing layer that further disables specific upper-triangle neurons to split the sequence as multiple independent sessions. The mixer serially alternates these two layers to support fine-grained preferences modeling, where the global one focuses on the long-range dependency in the whole sequence, and the local one calls for the short-term patterns in sessions. Experimental results on 12 datasets of different scales from 4 benchmarks elucidate that TriMLP consistently attains favorable accuracy/efficiency tradeoff over all validated datasets, where the average performance boost against several state-of-the-art baselines achieves up to 14.88%, and the maximum reduction of inference time reaches 23.73%. The intriguing properties render TriMLP a strong contender to the well-established RNN-, CNN-, and Transformer-based sequential recommenders. Code is available at https://github.com/jiangyiheng1/TriMLP .

Point-of-Interest (POI) recommendation, an important research hotspot in the field of urban computing, plays a crucial role in urban construction. While understanding the process of users’ travel decisions and exploring the causality of POI choosing is not easy due to the complex and diverse influencing factors in urban travel scenarios. Moreover, the spurious explanations caused by severe data sparsity, i.e., misrepresenting universal relevance as causality, may also hinder us from understanding users’ travel decisions. To this end, in this article, we propose a factor-level causal explanation generation framework based on counterfactual data augmentation for user travel decisions, named Factor-level Causal Explanation for User Travel Decisions (FCE-UTD), which can distinguish between true and false causal factors and generate true causal explanations. Specifically, we first assume that a user decision is composed of a set of several different factors. Then, by preserving the user decision structure with a joint counterfactual contrastive learning paradigm, we learn the representation of factors and detect the relevant factors. Next, we further identify true causal factors by constructing counterfactual decisions with a counterfactual representation generator, in particular, it can not only augment the dataset and mitigate the sparsity but also contribute to clarifying the causal factors from other false causal factors that may cause spurious explanations. Besides, a causal dependency learner is proposed to identify causal factors for each decision by learning causal dependency scores. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our approach in terms of check-in rate, fidelity, and downstream tasks under different behavior scenarios. The extra case studies also demonstrate the ability of FCE-UTD to generate causal explanations in POI choosing.

Job interviews are the most widely accepted method for companies to select suitable candidates, and a critical challenge is finding the right questions to ask job candidates. Moreover, there is a lack of integrated tools for automatically generating interview questions and recommending the right questions to interviewers. To this end, in this paper, we propose an intelligent system for assisting job interviews, namely, DuerQues. To build this system, we first investigate how to automatically generate skill-oriented interview questions in a scalable way by learning external knowledge from online knowledge-sharing communities. Along this line, we develop a novel distantly supervised skill entity recognition method to identify skill entities from large-scale search queries and web page titles with less need for human annotation. Additionally, we propose a neural generative model for generating skill-oriented interview questions. In particular, we introduce a data-driven solution to create high-quality training instances and design a learning algorithm to improve the performance of question generation. Furthermore, we exploit click-through data from query logs and design a recommender system for recommending suitable questions to interviewers. Specifically, we introduce a graph-enhanced algorithm to efficiently recommend suitable questions given a set of queried skills. Finally, extensive experiments on real-world datasets demonstrate the effectiveness of our DuerQues system in terms of the quality of generated skill-oriented questions and the performance of question recommendation.

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This article introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multi-channel prediction module, we transform entity and multi-event extraction into a single task of predicting token–event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F 1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework.

The recent development of recommender systems has a focus on collaborative ranking, which provides users with a sorted list rather than rating prediction. The sorted item lists can more directly reflect the preferences for users and usually perform better than rating prediction in practice. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” and unobserved data due to the precondition of the entire list permutation. To this end, in this article, we propose a novel setwise Bayesian approach for collaborative ranking, namely, SetRank, to inherently accommodate the characteristics of user feedback in recommender systems. SetRank aims to maximize the posterior probability of novel setwise preference structures and three implementations for SetRank are presented. We also theoretically prove that the bound of excess risk in SetRank can be proportional to \(\sqrt {M/N}\) , where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.

This article studies the problem of semi-supervised learning on graphs, which aims to incorporate ubiquitous unlabeled knowledge (e.g., graph topology, node attributes) with few-available labeled knowledge (e.g., node class) to alleviate the scarcity issue of supervised information on node classification. While promising results are achieved, existing works for this problem usually suffer from the poor balance of generalization and fitting ability due to the heavy reliance on labels or task-agnostic unsupervised information. To address the challenge, we propose a dual-channel framework for semi-supervised learning on G raphs via K nowledge T ransfer between independent supervised and unsupervised embedding spaces, namely, GKT. Specifically, we devise a dual-channel framework including a supervised model for learning the label probability of nodes and an unsupervised model for extracting information from massive unlabeled graph data. A knowledge transfer head is proposed to bridge the gap between the generalization and fitting capability of the two models. We use the unsupervised information to reconstruct batch-graphs to smooth the label probability distribution on the graphs to improve the generalization of prediction. We also adaptively adjust the reconstructed graphs by encouraging the label-related connections to solidify the fitting ability. Since the optimization of the supervised channel with knowledge transfer contains that of the unsupervised channel as a constraint and vice versa, we then propose a meta-learning-based method to solve the bi-level optimization problem, which avoids the negative transfer and further improves the model’s performance. Finally, extensive experiments validate the effectiveness of our proposed framework by comparing state-of-the-art algorithms.

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.

Recently, deep learning based methods, especially graph neural network (GNN), have made impressive progress on rating prediction problem in recommender systems. However, the performance of existing methods drops quickly in the cold start scenario. More importantly, such methods are unable to learn the preference embedding of a strict cold start user/item since there is no interaction for this user/item. In this work, we develop a novel framework Attribute Graph Neural Networks (AGNN) by exploiting the attribute graph rather than the commonly used interaction graph. AGNN can produce the preference embedding for a strict cold user/item by learning on the distribution of attributes with an extended variational auto-encoder (eVAE) structure. It also contains a new graph neural network variant (gated-GNN) to effectively aggregate various attributes of different dimensions in a neighborhood. Empirical results demonstrate that AGNN achieves the new state-of-the-art performance.

Urban villages (UVs) refer to the underdeveloped informal settlement falling behind the rapid urbanization in a city. Since there are high levels of social inequality and social risks in these UVs, it is critical for city managers to discover all UVs for making appropriate renovation policies. Existing approaches to detecting UVs are labor-intensive or have not fully addressed the unique challenges in UV detection such as the scarcity of labeled UVs and the diverse urban patterns in different regions. To this end, we first build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area. Finally, we conduct extensive experiments in three cities to demonstrate the effectiveness of our approach.

Understanding the semantic structure of resumes plays an important role for various intelligent recruitment related applications. However, due to the unique characteristics of resume documents (e.g., diverse writing styles and multi-page) and the lack of labeled data, it has been a long-standing challenge to effectively extract the structural information of resumes through machine learning models. While considerable efforts have been made in this direction, existing methods only focus on the textual information in the document where the rich multi-modal information (e.g., the visual and layout information) is largely ignored. To this end, in this paper, we propose ResuFormer for understanding the semantic structure of resumes. Specifically, ResuFormer focuses on two typical tasks in this direction, namely resume block classification and intra-block information extraction respectively. For the first task, we propose a multi-modal pre-training model with a hierarchical Transformer encoder, in which we design three self-supervised training objectives, i.e., masked layout-language model, self-supervised contrastive learning and dynamic next-sentence prediction, to pre-train the model parameters, and fine-tune the model only using a small amount of training data. For the second task, we introduce a self-distillation based self-training learning framework to make the distantly supervised model more robust to the noise data. Finally, extensive experiments conducted on real-world resume datasets have clearly validated the performance of our ResuFormer compared with state-of-the-art (SOTA) baselines.

Medical tests are crucial for treatment decision making. However, over-testing can often occur in any medical speciality or level of expertise. Since over-testing usually results in a financial burden for patients and is also a waste of medical resources, this naturally leads to the question: which medical test items (MTIs) are necessary and should be prioritized for the target patients? It is a nontrivial task to identify the right MTIs due to the diversified health status of patients and the complicated prerequisites of therapies. To this end, in this paper, we propose a data-driven approach to evaluate the priority which should be given to MTIs by modeling the relationships between MTIs and therapies. Specifically, we first develop a dual hierarchical topic model (DHTM), which views the adopted hierarchical therapies as labeled topics and the MTI reports, i.e., the set of hierarchical attribute-value pairs (AVPs), as documents. Then, with the therapy-AVP distribution and the partial MTI reports of the target patient, we can scope the candidate therapies, which are further utilized to evaluate the accumulated gain of MTIs to be tested. Moreover, the next MTI recommendation is conducted based on the gains. Finally, extensive experiments on real-world medical data validate the effectiveness of our approach, and some interesting observations are also provided. The code is available at https://github.com/mtirec/MtiRec.

Graph Neural Networks (GNNs) generalize conventional neural networks to graph-structured data and have received considerable attention owing to their impressive performance. In spite of the notable successes, the performance of Euclidean models is inherently bounded and limited by the representation ability of Euclidean geometry, especially when it comes to datasets with highly non-Euclidean latent anatomy. Recently, hyperbolic spaces have emerged as a promising alternative for processing graph data with tree-like structure or power-law distribution and a surge of works on either methods or novel applications have been seen. Unlike Euclidean space, which expands polynomially, hyperbolic space grows exponentially with its radius, making it more suitable for modeling complex real-world data. Hence, it gains natural advantages in abstracting tree-like graphs with a hierarchical organization or power-law distribution.

Recent years have witnessed the rapid development of machine-learning-based intelligent recruitment services. Along this line, a large number of emerging models have been proposed, achieving remarkable performance in various tasks, such as person-job fit, job classification and salary prediction. However, existing studies are usually domain/task specific, which significantly hinders the adaptation of models for different industries/tasks with limited training data. To this end, in this paper, we propose a novel skill-aware prompt-based pretraining framework, namely RecruitPro, which is capable of learning unified representations on the recruitment data and adapting for various downstream tasks of intelligent recruitment services. To be specific, we first present a contextualized embedding model that is pretrained on a large-scale recruitment dataset. Then, we construct 13 downstream benchmark tasks that are representative in the recruitment process. Along this line, we propose a skill-aware prompt learning module to enhance the adaptability of the pretrained model on downstream tasks. This module includes a skill-related prompt, which is designed to explore key semantic information (i.e., skills) from recruitment text, and a task-related prompt, which is designed to bridge the gap between the pretrained model and different downstream tasks. Moreover, we propose a strategy for extracting potential skills to further improve the performance of our skill-aware prompt learning module. Finally, extensive experiments have clearly demonstrated the effectiveness of RecruitPro. In addition, a case study has been presented to discuss the privacy preserving issue of our RecruitPro.

Time-of-arrival estimation or Estimated Time of Arrival (ETA) has become an indispensable building block of modern intelligent transportation systems. While many efforts have been made for time-of-arrival estimation, most of them have scalability and robustness issues when dealing with real-world large-scale ETA scenarios, where billions of vehicle trajectories and ETA requests have been continuously generating every day. To this end, in this paper, we propose a robust and scalable incremental ETA learning framework, iETA, to continuously exploit spatio-temporal traffic patterns from massive floating-car data and thus achieve better estimation performances. Specifically, we first build an incremental travel time predictor that can be incrementally updated based on newly generated traffic data. The incremental travel time predictor not only reduces the overall learning overhead but also improves the model's robustness toward urban traffic distribution shifts. Then, we propose a historical traffic knowledge consolidation module to preserve critical spatio-temporal knowledge from previous ETA predictors under the incremental learning setting. Moreover, to reduce interference induced by low-quality traffic data, we propose an adversarial training module to improve the learning robustness by proactively mitigating and resisting traffic noise perturbations. Finally, extensive experiments demonstrate the effectiveness and efficiency of the proposed system against state-of-the-art baselines in large-scale ETA scenarios. Most importantly, iETA has been deployed on the Didi Chuxing platform, handling real-time billions of ETA queries every day, and substantially improves the prediction accuracy.

Popularity prediction, which aims to forecast how many users would like to interact with a target item or online content in the future, can help online shopping or social media platforms to identify popular items or digital contents. Many efforts have been made to study how the multi-faceted factors, such as item features, user preferences, and social influence, affect user-item interactions, but little work has focused on the evolutionary dynamics of these factors for individuals or groups. In that light, this paper develops a community-based dynamic graph learning method for popularity prediction. First, a dynamic graph learning framework is proposed to maintain a dynamic representation for each item or user entity and update the representations according to the newly observed user-item interactions. Second, a community detection module is designed to capture the evolving community structures and identify the most influential nodes. More importantly, our framework leverages a community-level message passing during the learning process to balance local and global information propagation. Finally, we predict the popularity of the target item or online content based on the learned representations. Our experimental results based on three real-world datasets demonstrate that the proposed method achieves better performance than the baselines. Our method could not only model the changes in a user's preferences, but also capture how the communities evolve over time.

Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios. Recent studies find that off-the-shelf Graph Neural Networks (GNNs) would under-represent minor class samples. We investigate this phenomenon and discover that the subspaces of minor classes being squeezed by those of the major ones in the latent space is the main cause of this failure. We are naturally inspired to enlarge the decision boundaries of minor classes and propose a general framework GraphSHA by Synthesizing HArder minor samples. Furthermore, to avoid the enlarged minor boundary violating the subspaces of neighbor classes, we also propose a module called SemiMixup to transmit enlarged boundary information to the interior of the minor classes while blocking information propagation from minor classes to neighbor classes. Empirically, GraphSHA shows its effectiveness in enlarging the decision boundaries of minor classes, as it outperforms various baseline methods in class-imbalanced node classification with different GNN backbone encoders over seven public benchmark datasets. Code is avilable at https://github.com/wenzhilics/GraphSHA.

Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting'' learning scheme. However, we observe that unlike CL in computer vision domain, CL in graph domain performs decently even without augmentation. We conduct a systematic analysis of this phenomenon and argue that homophily, i.e., the principle that "like attracts like'', plays a key role in the success of graph CL. Inspired to leverage this property explicitly, we propose HomoGCL, a model-agnostic framework to expand the positive set using neighbor nodes with neighbor-specific significances. Theoretically, HomoGCL introduces a stricter lower bound of the mutual information between raw node features and node embeddings in augmented views. Furthermore, HomoGCL can be combined with existing graph CL models in a plug-and-play way with light extra computational overhead. Extensive experiments demonstrate that HomoGCL yields multiple state-of-the-art results across six public datasets and consistently brings notable performance improvements when applied to various graph CL methods. Code is avilable at https://github.com/wenzhilics/HomoGCL.

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation learning and further handle the relationship sparsity. Finally, experiments on four real-world datasets demonstrate the superiority of our method over several state-of-the-art approaches.

The timely prediction of bankruptcy is highly desirable to guarantee an upward spiral for overall societal well-being. By extracting multifaceted information from the business interaction networks, Graph Neural Networks (GNNs) may be able to automatically make more informed predictions for bankruptcy, as compared to methods that rely heavily on abundant manpower to a large extent. Yet in real applications, bankruptcy prediction faces the key issue of quantity-imbalance: data usually comes with a long-tailed distribution wherein bankrupt corporates occupy the least of the data proportion but are our target to be identified. Apart from that, the topology-imbalance issue behind graph-structural data exacerbates prediction deterioration: feature propagation is dominated by non-bankrupt nodes through messages passing between nodes; thus, bankrupt nodes receive highly confusing information and could be easily assimilated by nearby non-bankrupt nodes. Unfortunately, the existing GNN methods are not immune to these two imbalance issues. To tackle the challenging but practically useful scenario, we propose a novel bankruptcy prediction model called the Quantity and Topology Imbalance-Aware Heterogeneous Graph Neural Network (QTIAH-GNN) to boost the final performance. Specifically, QTIAH-GNN employs the multi-hierarchy label-aware neighbor selection to conquer the topology-imbalance issue by using the class-semantic representation and the learnable parameterized similarity metric, and employs the imbalance-oriented loss to obtain the optimal tradeoff between the accuracies of the majority and minority classes. In experiments, we evaluate the proposed QTIAH-GNN on two large-scale, real-world datasets. The results show that QTIAH-GNN outperforms other state-of-the-art baselines in terms of prediction accuracy with superior efficiency and generalization ability, has stronger robustness to data imbalance, and provides meaningful model interpretation.

Recent years have witnessed the rapid development of the Cooperative Vehicle Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and autonomous vehicles (AVs) can share information among each other and work collaboratively to provide safer and more comfortable transportation experience to human beings. While many efforts have been made to develop efficient and sustainable CVIS solutions, existing approaches on urban intersections heavily rely on domain knowledge and physical assumptions, preventing them from being practically applied. To this end, this paper proposes NavTL, a learning-based framework to jointly control traffic signal plans and autonomous vehicle rerouting in mixed traffic scenarios where human-driven vehicles and AVs co-exist. The objective is to improve travel efficiency and reduce total travel time by minimizing congestion at the intersections while guiding AVs to avoid the temporally congested roads. Specifically, we design a graph-enhanced multi-agent decentralized bi-directional hierarchical reinforcement learning framework by regarding TLs as manager agents and AVs as worker agents. At lower temporal resolution timesteps, each manager sets a goal for the workers within its controlled region. Simultaneously, managers learn to take the signal actions based on the observation from the environment as well as an intention information extracted from its workers. At higher temporal resolution timesteps, each worker makes rerouting decisions along its way to the destination based on its observation from the environment, an intention-enhanced manager state representation, and a goal from its present manager. Finally, extensive experiments on one synthetic and two real-world network-level datasets demonstrate the effectiveness of our proposed framework in terms of improving travel efficiency.

In this paper, we propose an adaptive learning paradigm for resource-constrained cross-device federated learning, in which heterogeneous local submodels with varying resources can be jointly trained to produce a global model. Different from existing studies, the submodel structures of different clients are formed by arbitrarily assigned neurons according to their local resources. Along this line, we first design a general resource-adaptive federated learning algorithm, namely RA-Fed, and rigorously prove its convergence with asymptotically optimal rate O(1/√Γ*TQ) under loose assumptions. Furthermore, to address both submodels heterogeneity and data heterogeneity challenges under non-uniform training, we come up with a new server aggregation mechanism RAM-Fed with the same theoretically proved convergence rate. Moreover, we shed light on several key factors impacting convergence, such as minimum coverage rate, data heterogeneity level, submodel induced noises. Finally, we conduct extensive experiments on two types of tasks with three widely used datasets under different experimental settings. Compared with the state-of-the-arts, our methods improve the accuracy up to 10% on average. Particularly, when submodels jointly train with 50% parameters, RAM-Fed achieves comparable accuracy to FedAvg trained with the full model.

Graph Neural Networks (GNNs) have been broadly applied in many urban applications upon formulating a city as an urban graph whose nodes are urban objects like regions or points of interest. Recently, a few enhanced GNN architectures have been developed to tackle heterophily graphs where connected nodes are dissimilar. However, urban graphs usually can be observed to possess a unique spatial heterophily property; that is, the dissimilarity of neighbors at different spatial distances can exhibit great diversity. This property has not been explored, while it often exists. To this end, in this paper, we propose a metric, named Spatial Diversity Score, to quantitatively measure the spatial heterophily and show how it can influence the performance of GNNs. Indeed, our experimental investigation clearly shows that existing heterophilic GNNs are still deficient in handling the urban graph with high spatial diversity score. This, in turn, may degrade their effectiveness in urban applications. Along this line, we propose a Spatial Heterophily Aware Graph Neural Network (SHGNN), to tackle the spatial diversity of heterophily of urban graphs. Based on the key observation that spatially close neighbors on the urban graph present a more similar mode of difference to the central node, we first design a rotation-scaling spatial aggregation module, whose core idea is to properly group the spatially close neighbors and separately process each group with less diversity inside. Then, a heterophily-sensitive spatial interaction module is designed to adaptively capture the commonality and diverse dissimilarity in different spatial groups. Extensive experiments on three real-world urban datasets demonstrate the superiority of our SHGNN over several its competitors.

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 4th International Workshop on Talent and Management Computing (TMC'2023), is to bring together researchers and practitioners to discuss both the critical problems faced by talent and management related domains, and potential data-driven solutions by leveraging state-of-the-art data mining technologies.

With the rapid development of enterprise Learning Management Systems (LMS), more and more companies are trying to build enterprise training and course learning platforms for promoting the career development of employees. Indeed, through course learning, many employees have the opportunity to improve their knowledge and skills. For these systems, a major issue is how to recommend learning plans, i.e., a set of courses arranged in the order they should be learned, that can help employees improve their work performance. Existing studies mainly focus on recommending courses that users are most likely to click on by capturing their learning preferences. However, the learning preference of employees may not be the right fit for their career development, and thus it may not necessarily mean their work performance can be improved accordingly. Furthermore, how to capture the mutual correlation and sequential effects between courses, and ensure the rationality of the generated results, is also a major challenge. To this end, in this paper, we propose the Generative Learning plAn recommenDation (GLAD) framework, which can generate personalized learning plans for employees to help them improve their work performance. Specifically, we first design a performance predictor and a rationality discriminator, which have the same transformer-based model architecture, but with totally different parameters and functionalities. In particular, the performance predictor is trained for predicting the work performance of employees based on their work profiles and historical learning records, while the rationality discriminator aims to evaluate the rationality of the generated results. Then, we design a learning plan generator based on the gated transformer and the cross-attention mechanism for learning plan generation. We calculate the weighted sum of the output from the performance predictor and the rationality discriminator as the reward, and we use Self-Critical Sequence Training (SCST) based policy gradient methods to train the generator following the Generative Adversarial Network (GAN) paradigm. Finally, extensive experiments on real-world data clearly validate the effectiveness of our GLAD framework compared with state-of-the-art baseline methods and reveal some interesting findings for talent management.

Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of tailor-made graph convolutions to capture structural and semantic information in heterogeneous graphs. However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing. Then we propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. To avoid the information loss caused by the single vector node representation, we first design a sequential node representation learning mechanism to represent each node as a sequence of meta-path representations during the node message passing. Then we propose a heterogeneous representation fusion module, empowering Seq-HGNN to identify important meta-paths and aggregate their representations into a compact one. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB). Experimental results show that our proposed method outperforms state-of-the-art baselines in both accuracy and efficiency. The source code is available at https://github.com/nobrowning/SEQ_HGNN.

In recent years, the next-POI recommendation has become a trending research topic in the field of trajectory data mining. For protection of user privacy, users’ complete GPS trajectories are difficult to obtain. The check-in information posted by users on social networks has become an important data source for Spatio-temporal Trajectory research. However, state-of-the-art methods neglect the social meaning and the information dissemination function of check-in behavior. The social meaning is an important reason why users are willing to post check-in on social networks, and the information dissemination function means, users can affect each other’s behavior by check-ins. The above characteristics of the check-in behavior make it different from the visiting behavior. We consider a new problem of predicting the next check-in behavior including the check-in time, the POI (point-of-interest) where the check-in is located, functional semantics of the POI, and so on. To solve the proposed problem, we build a multi-task learning model called DPMTM, and a pre-training module is designed to extract dynamic social semantics of check-in behaviors. Our results show that the DPMTM model works well in the check-in behavior problem.

Urban vibrancy describes the prosperity, diversity, and accessibility of urban areas, which is vital to a city’s socio-economic development and sustainability. While many efforts have been made for statically measuring and evaluating urban vibrancy, there are few studies on the evolutionary process of urban vibrancy, yet we know little about the relationship between urban vibrancy evolution and sophisticated spatiotemporal dynamics. In this article, we make use of multi-sourced urban data to develop a data-driven framework, U-Evolve , to investigate urban vibrancy evolution. Specifically, we first exploit the spatiotemporal characteristics of urban areas to create multi-view time-dependent graphs. Then, we analyze the contextual features and graph patterns of multi-view time-dependent graphs in terms of informing future urban vibrancy variations. Our analysis validates the informativeness of multi-view time-dependent graphs for characterizing and informing future urban vibrancy evolution. After that, we construct a feature based model to forecast future urban vibrancy evolution and quantify each feature’s importance. Moreover, to further enhance the forecasting effectiveness, we propose a graph learning based model to capture spatiotemporal autocorrelation of urban areas based on multi-view time-dependent graphs in an end-to-end manner. Finally, extensive experiments on two metropolises, Beijing and Shanghai, demonstrate the effectiveness of our forecasting models. The U-Evolve framework has also been deployed in the production environment to deliver real-world urban development and planning insights for various cities in China.

We study the problem of balancing effectiveness and efficiency in automated feature selection. Feature selection is to find an optimal feature subset from large feature space. After exploring many feature selection methods, we observe a computational dilemma: 1) traditional feature selection (e.g., mRMR) is mostly efficient, but difficult to identify the best subset; 2) the emerging reinforced feature selection automatically navigates feature space to search the best subset, but is usually inefficient. Are automation and efficiency always apart from each other? Can we bridge the gap between effectiveness and efficiency under automation? Motivated by this dilemma, we aim to develop a novel feature space navigation method. In our preliminary work, we leveraged interactive reinforcement learning to accelerate feature selection by external trainer-agent interaction. Our preliminary work can be significantly improved by modeling the structured knowledge of its downstream task (e.g., decision tree) as learning feedback. In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF). Specifically, IRL is to create an interactive feature selection loop and DTF is to feed structured feature knowledge back to the loop. The DTF improves IRL from two aspects. First, the tree-structured feature hierarchy generated by decision tree is leveraged to improve state representation. In particular, we represent the selected feature subset as an undirected graph of feature-feature correlations and a directed tree of decision features. We propose a new embedding method capable of empowering Graph Convolutional Network (GCN) to jointly learn state representation from both the graph and the tree. Second, the tree-structured feature hierarchy is exploited to develop a new reward scheme. In particular, we personalize reward assignment of agents based on decision tree feature importance. In addition, observing agents’ actions can also be a feedback, we devise another new reward scheme, to weigh and assign reward based on the selected frequency ratio of each agent in historical action records. Finally, we present extensive experiments with real-world datasets to demonstrate the improved performances of our method.

Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose a multi-view multi-adversarial approach, entitled MasterGNN$^{+}$, to jointly predict air quality and weather conditions. First, we devise a multi-view graph learning block to model spatial autocorrelation based on geographical distance and environmental context. Then, a dedicated evolved recurrent network is proposed to dynamically capture the long-range and independent temporal autocorrelation for each monitoring station and time slot. After that, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we present an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN$^{+}$achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.

Hierarchical multi-label classification (HMC) deals with the problem of assigning each entity to multiple classes with a taxonomic structure (e.g., tree). Within this structure, classes at different levels tend to have dependencies under the hierarchy constraints. However, most prior studies for HMC tasks tend to ignore the class dependencies within the hierarchy. Moreover, most existing methods generate incoherent predictions and do not satisfy the hierarchy constraint. To this end, based on previously developed HARNN, we propose a general framework, HmcNet, for introducing explicit and implicit class hierarchy constraints to generate coherent predictions. We develop an efficient Prune-based Coherent Prediction (PCP) strategy for the optimal paths selection, which produces coherent predictions in a principled way. HmcNet can be well explained from two perspectives. First, it develops the Hierarchical Attention-based Memory (HAM) unit with implicit class hierarchy constraints to capture class dependencies more intuitively; Second, it subsumes explicit class hierarchy constraints during training and inference phases and generates coherent predictions in a consistent manner. Finally, extensive experimental results on six real-world datasets demonstrate the effectiveness and interpretability of the HmcNet frameworks. To facilitate future research, our code has been made publicly available.

While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namelyconsistency and complementarity network(CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet.

Feature selection is a critical step in machine learning that selects the most important features for a subsequent prediction task. Effective feature selection can help to reduce dimensionality, improve prediction accuracy, and increase result comprehensibility. It is traditionally challenging to find the optimal feature subset from the feature subset space as the space could be very large. While much effort has been made on feature selection, reinforcement learning can provide a new perspective towards a more globally-optimal searching strategy. In the preliminary work, we propose a multi-agent reinforcement learning framework for the feature selection problem. Specifically, we first reformulate feature selection with a reinforcement learning framework by regarding each feature as an agent. Besides, we obtain the state of the environment in three ways, i.e., statistic description, autoencoder, and graph convolutional network (GCN), in order to derive a fixed-length state representation as the input of reinforcement learning. In addition, we study how the coordination among feature agents can be improved by a more effective reward scheme. Also, we provide a GMM-based generative rectified sampling strategy to accelerate the convergence of multi-agent reinforcement learning. Our method searches the feature subset space more globally and can be easily adapted to real-time scenarios due to the nature of reinforcement learning. In the extended version, we further accelerate the framework from two aspects. From the sampling aspect, we show the indirect acceleration by proposing a rank-based softmax sampling strategy. From the exploration aspect, we show the direct acceleration by proposing an interactive reinforcement learning (IRL)-based exploration strategy. Extensive experimental results show the significant improvement of the proposed method over conventional approaches.

Public transportation plays a critical role in peoples daily life. It has been proven that public transportation is more environmentally sustainable, efficient, and economical than any other forms of travel. However, due to the increasing expansion of transportation networks and more complex travel situations, people are having difficulties in efficiently finding the most preferred route from one place to another through public transportation systems for both intra-city and inter-city trips. To this end, in this paper, we present Polestar++, a data-driven engine for intelligent and efficient public transportation routing. Specifically, we first propose a novel hierarchical public transportation graph(HPTG) to model both intra-city and inter-city public transportation in terms of various travel costs, such as time or distance. Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation. After that, we propose a two-pass route candidate ranking module to capture user preferences under dynamic travel situations. Finally, experiments on two real-world data sets demonstrate the advantages of Polestar++ in terms of both efficiency and effectiveness. To date, Polestar++ is servicing over 330 cities, answers over a hundred millions of queries each day, and achieves substantial improvement of user click ratio.

Conversational recommendation casts the recommendation problem as a dialog-based interactive task, which could acquire user interest more efficiently and effectively by allowing users to express what they like. In this work, we move a step towards a new conversational recommendation task that is more suitable for real-world applications. In this task, the recommender proactively and naturally lead a dialog from non-recommendation content to approach an item being of interest to users, and allow users to ask questions for better support of user decisions. The challenge of this task lies in how to effectively control the dialog flow to complete the recommendation while appropriately responding to user utterances. To address this challenge, we first construct a Chinese recommendation dialog dataset DuRecDial. We then propose a two-stage Multi-Goal driven Conversation Generation framework, MGCG. In particular, the goal planning module leverages the global graph structure information and local goal-sequence information to effectively control the dialog flow step by step. The goal-guided responding module can produce an in-depth dialog about each goal by fully exploiting hierarchical goal information for response retrieval or generation. Results on DuRecDial demonstrate that MGCG can lead the dialog more proactively and naturally, and complete the recommendation task more effectively.

Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of these occurrences in a wide range of disciplines - for instance, security, finance, and medicine. For this reason, anomaly detection, which aims to identify these rare observations, has become one of the most vital tasks in the world and has shown its power in preventing detrimental events, such as financial fraud, network intrusions, and social spam. The detection task is typically solved by identifying outlying data points in the feature space, which, inherently, overlooks the relational information in real-world data. At the same time, graphs have been prevalently used to represent the structural/relational information, which raises thegraph anomaly detection problem- identifying anomalous graph objects (i.e., nodes, edges and sub-graphs) in a single graph, or anomalous graphs in a set/database of graphs. Conventional anomaly detection techniques cannot tackle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.). However, thanks to the advent of deep learning in breaking these limitations, graph anomaly detection with deep learning has received a growing attention recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. Specifically, we provide a taxonomy that follows a task-driven strategy and categorizes existing work according to the anomalous graph objects that they can detect. We especially focus on the challenges in this research area and discuss the key intuitions, technical details as well as relative strengths and weaknesses of various techniques in each category. From the survey results, we highlight 12 future research directions spanning unsolved and emerging problems introduced by graph data, anomaly detection, deep learning and real-world applications. Additionally, to provide a wealth of useful resources for future studies, we have compiled a set of open-source implementations, public datasets, and commonly-used evaluation metrics. With this survey, our goal is to create a “one-stop-shop” that provides a unified understanding of the problem categories and existing approaches, publicly available hands-on resources, and high-impact open challenges for graph anomaly detection using deep learning.

Unsupervised domain adaptation (UDA) requires source domain samples with clean ground truth labels during training. Accurately labeling a large number of source domain samples is time-consuming and laborious. An alternative is to utilize samples with noisy labels for training. However, training with noisy labels can greatly reduce the performance of UDA. In this paper, we address the problem that learning UDA models only with access to noisy labels and propose a novel method called robust local preserving and global aligning network (RLPGA). RLPGA improves the robustness of the label noise from two aspects. One is learning a classifier by a robust informative-theoretic-based loss function. The other is constructing two adjacency weight matrices and two negative weight matrices by the proposed local preserving module to preserve the local topology structures of input data. We conduct theoretical analysis on the robustness of the proposed RLPGA and prove that the robust informative-theoretic-based loss and the local preserving module are beneficial to reduce the empirical risk of the target domain. A series of empirical studies show the effectiveness of our proposed RLPGA.

A job description shows the responsibilities of the job position and the skill requirements for the job. An effective job description will help employers to identify the right talents for the job, and give a clear understanding to candidates of what their duties and qualifications for a particular position would be. In this paper, we investigate how to automate the process to generate job descriptions with less human intervention. We propose an end-to-end capability-aware neural job description generation framework, namely Cajon, to facilitate the writing of job description. Specifically, we first propose a novel capability-aware neural topic model to distill the various capability information from the larger-scale recruitment data. Also, an encoder-decoder recurrent neural network is designed for enabling the job description generation. In particular, the capability-aware attention and copy mechanisms are proposed to guide the generation process to ensure the generated job descriptions can comprehensively cover relevant and representative capability requirements for the job. Moreover, we propose a capability-aware policy gradient training algorithm to further enhance the rationality of the generated job description. Finally, extensive experiments on real-world recruitment data clearly show our Cajon framework can help to generate more effective job descriptions in an interpretable way

Dynamic systems are often monitored with multivariate time series where each dimension represents a local component measured through a (virtual) sensor. Performing accurate diagnostic for dynamic systems while simultaneously taking into account their similarities/distinctions, is a non-trivial task. To this end, we develop an adaptive regularization approach to learning sparse connectivity structures in complex dynamic systems. The learned connectivity networks shed lights on the structural compositions of the system and hence can serve as highly informative inputs for various machine learning tasks such as classification. In particular, we focus on high-dimensional and semi-supervised learning scenarios and present a joint learning approach to recover system-wise connectivity patterns by adaptively constructing a shared, sparsity-inducing regularization template across all systems. The shared template can be physically interpreted and used as a modeling template for analyzing new systems. Moreover, our approach has the flexibility to incorporate supervising information such as must-links and cannot-links for constructing regularization templates. Overall, our approach, named sparse adaptive regularization (SAR), can extract structure-related connectivity features efficiently and effectively, and result in significant improvements for machine learning tasks in dynamic systems. We benchmark our approach against the state-of-the-art methods with real-world data. Our results demonstrate the superiority of our approach.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement, i.e. events are scheduled at the reasonable time to attract maximum number of participants. Existing approaches usually focus on assigning a set of events organized by the same group to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this paper, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. Unfortunately, we find that the PETA problem is NP-hard due to the global conflict constraints on events. Thus, we propose design a greedy algorithm and two approximation algorithms to solve the PETA problem. Finally, we conduct extensive experiments on both real and synthetic datasets to test the effectiveness and efficiency of the proposed algorithms.

Person-Organization fit (P-O fit) refers to the compatibility between employees and their organizations. The study of P-O fit is important for enhancing proactive talent management. While considerable efforts have been made in this direction, it still lacks a quantitative and holistic way for measuring P-O fit and its impact on talent management. To this end, in this paper, we propose a novel data-driven neural network approach for dynamically modeling the compatibility in P-O fit and its meaningful relationships with two critical issues in talent management, namely talent turnover and job performance. Specifically, inspired by the practical management scenarios, we creatively propose a novel neural-network-based P-O fit model. We first designed three kinds of organization-aware compatibility features extraction layers for measuring P-O fit. Then, to capture the dynamic nature of P-O fit and its consequent impact, we further exploit an adapted Recurrent Neural Network with attention mechanism to model the temporal information of P-O fit. Finally, we compare our approach with a number of state-of-the-art baseline methods on real-world talent data. Experimental results clearly demonstrate the effectiveness in terms of turnover and job performance prediction. Moreover, we show some interesting indicators of talent management through the visualizing some network layers.

Mobile user profiling refers to the efforts of extracting users’ characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user's profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users’ mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.

The peer merit review of research proposals has been the major mechanism to decide grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principle investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs to learn representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

Clinical risk prediction based on Electronic Health Records (EHR) can assist doctors in better judgment and can make sense of early diagnosis. However, the prediction performance heavily relies on effective representations from multi-dimensional time-series EHR data. Existing solutions usually focus on temporal features or inherent relations between clinical event variables or extract both information in two separate phases. This usually leads to insufficient patient feature information and results in poor prediction performance. Moreover, existing methods based on Heterogeneous Graph Neural Network usually require manual selection of proper Meta-Paths. To solve these problems, we propose the Time-aware Context-Gated Graph Attention Network (T-ContextGGAN). Specifically, we design a GNN based module with Time-aware Meta-Paths and self-attention mechanism to extract both temporal semantic information and inherent relations of EHR data simultaneously and perform automatic Meta-Path selection. To evaluate the proposed model, we extract the first 48 hour EHR data in the first Intensive Care Unit (ICU) admission of three different tasks from two open-source datasets and model various clinical variables on the proposed EHRGraph. Extensive experimental results show the proposed model can effectively extract informative features, and outperform existing state-of-art models in terms of various prediction measures. Our code is available in https://github.com/OwlCitizen/TContext-GGAN.

Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data, which emerges unknown classes sequentially. Different from traditional closed set learning, CIL has two main challenges: (1) Novel class detection. The initial training data only contains incomplete classes, and streaming test data will accept unknown classes. Therefore, the model needs to not only accurately classify known classes, but also effectively detect unknown classes; (2) Model expansion. After the novel classes are detected, the model needs to be updated without re-training using the entire previous data. However, traditional CIL methods have not fully considered these two challenges. To this end, we propose a Class-Incremental Learning without Forgetting (CILF) framework. In detail, CILF designs to regularize classification with decoupled prototype based loss, which can improve the intra-class and inter-class structure significantly, and acquire a compact embedding representation for novel class detection in result. Then, CILF employs a learnable curriculum clustering operator to estimate the number of semantic clusters via fine-tuning the learned network, in which curriculum operator can adaptively learn the embedding in self-taught form. Last, with the labeled streaming test data, CILF can update the network with robust regularization to mitigate the catastrophic forgetting.

Corporate relative valuation (CRV) refers to the process of comparing a company's value from company products, core staff and other related information, so that we can assess the company's market value, which is critical for venture capital firms. Traditionally, relative valuation methods heavily rely on tedious and expensive human efforts, especially for non-publicly listed companies. However, the availability of information about company's invisible assets, such as patents, talent, and investors, enables a new paradigm for learning and evaluating corporate relative values automatically. Indeed, in this paper, we reveal that, if the companies and their core members are formed as a heterogeneous graph and the attributes of different nodes include semantically-rich multi-modal data, it is able to extract a latent embedding for each company. Along this line, we develop an end-to-end heterogeneous multi-modal graph neural network method, named HM$^2$. Specifically, HM$^2$firstly perform the representation learning for heterogeneous neighbors of input company by taking relationships among nodes into consideration, which aggregates node attributes via linkage-aware multi-head attention mechanism, rather than multi-instance based methods. Then, HM$^2$adopts the self-attention network to aggregate different modal embeddings for final prediction, and employs dynamic triplet loss with embeddings of competitors as the constraint.

Temporal networks arise in various real-world scenarios, including social networks, user-item networks, traffic networks, financial transaction networks, etc. Modeling the dynamics of temporal networks is of importance as it describes how the networks evolve, which helps to understand and predict the behavior of the systems. There has been a lot of research on temporal network representation learning so far. Nonetheless, most of them are based on euclidean geometry, which fails to encode the underlying hierarchical layout or scale-free property of the real-world temporal network. Encouragingly, hyperbolic geometry excels in preserving both node similarity and network hierarchies. In the preliminary work, we proposed a hyperbolic temporal graph network (HTGN) on the Poincaré ball model, taking advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry. HTGN moves the temporal network embedding into hyperbolic space and employs the hyperbolic graph neural network and hyperbolic gated recurrent neural network to capture spatial and temporal dynamics, respectively. In addition, two modules were further put forward to advance the performance: (1) hyperbolic temporal contextual self-attention to watch historical states and (2) hyperbolic temporal consistency to enforce the embeddings changing gradually. In this work, we further design a lightweight and efficient hyperbolic graph convolutional module that enables HTGN to scale to large-size graphs easily and flexibly handle datasets with different densities. Moreover, we investigate the hyperbolic temporal network embedding in the Lorentz model of hyperbolic geometry with regard to its numerical stability and optimization advantages. Extensive experiments demonstrate the effectiveness of the proposals as they consistently outperform the competing baselines on small-, medium-, and large-scale datasets.

Most existing approaches often utilize the pre-fixed structure and large number of labeled data for training complex deep models, which are difficult to implement on incremental scenarios. As a matter of fact, real-world data is always in stream form. Thereby, there exits two challenges for building incremental deep models: a)Capacity Scalability. The entire training data is not available before learning the task. It is a challenge to make the deep model structure scale with streaming data for flexible model evolution and faster convergence. b)Capacity Sustainability. The distribution streaming data usually changes in nature (concept drift), thus it is necessary to update the model while preserving previous knowledge for overcoming the catastrophic forgetting. To this end, we develop an incremental deep model (IDM), which expands the network structure according to streaming data and slows down forgetting with the adaptive fisher regularization. However, IDM ignores another significant challenge with streaming data: c)Capacity Demand. Training a deep model always needs a large amount of labeled data, whereas it is almost impossible to label all unlabeled instances in real time. The core problem is to select a small number of the most discriminative instances to label while keeping the predictive accuracy of the model. Thereby, we focus on the online semi-supervised learning scenario with abrupt changes in data distribution, and further improve IDM to a cost-effective incremental deep model (CE-IDM), which can adaptively select the most discriminative newly coming instances for query to reduce the manual labeling costs. Specifically, CE-IDM adopts a novel extensible deep network structure by using an extra attention model for hidden layers. Based on the adaptive attention weights, CE-IDM develops a novel instance selection criterion by jointly estimating unlabeled instances’ representative and informative degree to satisfy the capacity demand. With the newly labeled instances, CE-IDM can quickly update the model with adaptive depth from streaming data and enable capacity scalability. Also, we address capacity sustainability by exploiting the attention based fisher information matrix, which can slow down the forgetting in consequence. Finally, CE-IDM can deal with the three capacity challenges methioned above in a unified framework. We conduct extensive experiments on real-world data and show that CE-IDM outperforms the state-of-the-art methods with a substantial margin.

Every organization has organizational networks for exchange of ideas and information. It is believed that organizational network analysis (ONA) can help the business be more effective. While considerable research efforts have been made for visualizing and analyzing relationships in organizational networks, it lacks a holistic way to model the complex social structures and rich semantic information of these networks. Indeed, employee behaviors can occur across different communication platforms, such as email and instant messaging systems, which naturally lead to the multiplex structure of organizational social networks. Meanwhile, it is also a challenge to model the impact of semantic information, such as employee attributes and organization charts, and the collaboration relationships of employees. To this end, in this paper, we propose a Multiplex Attentive Network Embedding (MANE) approach for modeling organizational social networks in a holistic way. Specifically, we first develop a multiple attributed random walk approach to jointly model multiple networks, with the integration of external work information. Then, we preserve the network structure by maximizing the probability of predicting the central node based on the surrounding context nodes. In particular, we introduce an attention mechanism to assign a weight to each context node in the training process, according to its attributed relation and structural relation with the central node by utilizing the k-core algorithm and the shortest path algorithm. In this way, the embedding results can be kept consistent with their structural relationships. Furthermore, to solve some department-level tasks, we introduce an attentive relational transition method to learn the representation of departments in the organizational networks. Finally, we evaluate the performance of MANE with extensive experiments on real-world data for three important talent management tasks, namely employee performance prediction, employee turnover prediction and department performance prediction. We also conduct a link prediction task to validate the effectiveness of employee embedding. Experimental results clearly show the effectiveness and interpretability of MANE for organizational network analysis.

Representation learning on heterogeneous graphs aims to obtain meaningful node representations to facilitate various downstream tasks. Existing heterogeneous graph learning methods are primarily developed by following the propagation mechanism of node representations. There are few efforts on studying the role of relations for improving the learning of more fine-grained node representations. Indeed, it is important to collaboratively learn the semantic representations of relations and discern node representations with respect to different relation types. In this paper, we propose a novel Relation-aware Heterogeneous Graph Neural Network (R-HGNN), to learn node representations on heterogeneous graphs at a fine-grained level by considering relation-aware characteristics. Specifically, a dedicated graph convolution component is first designed to learn unique node representations from each relation-specific graph separately. Then, a cross-relation message passing module is developed to improve the interactions of node representations across different relations. Also, the relation representations are learned in a layer-wise manner to capture relation semantics, which are used to guide the node representation learning process. Moreover, a semantic fusing module is presented to aggregate relation-aware node representations into a compact representation with the learned relation representations. Experimental results on extensive graph learning tasks demonstrate that our approach could consistently outperform existing methods.

Existing studies have shown that user-item interaction data and social relation data can be jointly used for enhancing the performance of social recommendation. However, limited research has a focus on investigating how to deeply exploit different views of social interaction structures and rating behavior differences for further improving social recommendation. To this end, in this paper, we propose to integrate information from both semantic and structural views for social recommendation. Specifically, we first design a collective intelligence-based strategy to reveal high-quality implicit relations for both users and items. Then, by reformulating all available nodes and relations as a heterogeneous graph, we define multiple semantic metapaths to capture diverse preferences for comprehensive user and item representations. While various metapaths enlarge the representation capacity of users and items, they also introduce noise and irrelevant information. We recall that, for the user-item interaction graph, different structure sizes (e.g., local and global structures) provide diverse and complementary information for recommendation. Motivated by this, we propose a semantic and structural view fusion framework for social recommendation (S4Rec), which consists of a deep graph model and a wide attentive SVD (Singular Value Decomposition) model for rating prediction by taking the local and global structure as input and aggregating messages along the predefined metapaths. Finally, the two predicted results are adaptively fused to achieve the final both accurate and stable prediction. In addition, we treat the user's rating behavior difference as the relative position difference problem in the embedding space, and model it with TransH to improve the generalization ability of the main rating model. Extensive experiments on three open datasets demonstrate the superiority of our framework compared with state-of-the-art methods. Particularly, our model outperforms other baselines under different sparsity conditions, further validating the effectiveness on cold-start users. We release the source code athttps://github.com/lcwy220/Social-Recommendation.

Electric Vehicle (EV) has become preferable choices in modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find proper spots for charging because of the limited charging infrastructures and spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve charging experience over long-term horizons. In this paper, we propose RLCharge for intelligent EV charging station recommendation by jointly considering various long-term spatiotemporal factors. Specifically, by regarding each charging station as an agent, we formulate the problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with centralized training decentralized execution. Particularly, we propose a tailor designed centralized attentive critic with the delayed access strategy to coordinate the recommendation between geo-distributed agents during centralized training. Besides, we propose the spatio-temporal heterogeneous graph convolution module to handle the partial observability problem during decentralized execution. After that, to effectively optimize multiple divergent objectives, we develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction, and propose an adaptive imitation learning scheme to further accelerate and stabilize the policy convergence. Finally, extensive experiments on two real-world datasets demonstrate that RLCHARGE achieves the best comprehensive performance compared with ten baseline approaches.

As a key component of e-commerce computing, product representation learning (PRL) has benefitted a wide range of applications, such as product matching, search, and categorization. Nonetheless, existing PRL approaches have poor language understanding ability due to the insufficient ability in capturing contextualized semantics. Also, the learned representations by existing methods lack transferability for new products. Inspired by the recent development of pre-trained language models (PLMs), in this paper, we make the attempt to adapt PLMs for PRL to mitigate the above issues. To this end, we develop KINDLE, aKnowledge-drIven pre-trainiNg framework for proDuct representationLEarning, which can preserve the contextual semantics and multi-faceted product knowledgerobustlyandflexibly. Specifically, we first extend tranditional one-stage pre-training to a two-stage pre-training framework, i.e., language acquisition and knowledge acquisition stage respectively, in which we exploit a deliberate knowledge encoder to ensure a smooth knowledge fusion into PLM without interfering its original function. Then a hierarchical skeleton attention compatible with PLM is introduced to capture the key information of products. In addition, we propose a multi-objective heterogeneous embedding method to represent thousands of knowledge elements. This helps KINDLE calibrate knowledge noise and sparsity automatically by replacing isolated classes as training targets in knowledge acquisition tasks. Furthermore, an input-aware gating network is proposed to automatically select the most relevant knowledge for different downstream tasks. Finally, extensive experiments have demonstrated the advantages of KINDLE over the state-of-the-art baselines across three downstream tasks, product matching, personalized product search, and product classification, on both regular and zero-shot settings. As a key component of e-commerce computing, product representation learning (PRL) provides benefits for a variety of applications, including product matching, search, and categorization. The existing PRL approaches have poor language understanding ability due to their inability to capture contextualized semantics. In addition, the learned representations by existing methods are not easily transferable to new products. Inspired by the recent advance of pre-trained language models (PLMs), we make the attempt to adapt PLMs for PRL to mitigate the above issues. In this paper, we develop KINDLE, aKnowledge-drIven pre-trainiNg framework for proDuct representationLEarning, which can preserve the contextual semantics and multi-faceted product knowledgerobustlyandflexibly. Specifically, we first extend traditional one-stage pre-training to a two-stage pre-training framework, and exploit a deliberate knowledge encoder to ensure a smooth knowledge fusion into PLM. In addition, we propose a multi-objective heterogeneous embedding method to represent thousands of knowledge elements. This helps KINDLE calibrate knowledge noise and sparsity automatically by replacing isolated classes as training targets in knowledge acquisition tasks. Furthermore, an input-aware gating network is proposed to select the most relevant knowledge for different downstream tasks. Finally, extensive experiments have demonstrated the advantages of KINDLE over the state-of-the-art baselines across three downstream tasks.

Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in AI-related applications. Despite the large sizes, existing KGs are far from complete and comprehensive. In order to continuously enrich KGs, automatic knowledge construction and update mechanisms are usually utilized, which inevitably bring in plenty of noise. However, most existing knowledge graph embedding (KGE) methods assume that all the triple facts in KGs are correct, and project both entities and relations into a low-dimensional space without considering noise and knowledge conflicts. This will lead to low-quality and unreliable representations of KGs. To this end, in this paper, we propose a general multi-task reinforcement learning framework, which can greatly alleviate the noisy data problem. In our framework, we exploit reinforcement learning for choosing high-quality knowledge triples while filtering out the noisy ones. Also, in order to take full advantage of the correlations among semantically similar relations, the triple selection processes of similar relations are trained in a collective way with multi-task learning. Moreover, we extend popular KGE models TransE, DistMult, ConvE and RotatE with the proposed framework. Finally, the experimental validation shows that our approach is able to enhance existing KGE models and can provide more robust representations of KGs in noisy scenarios.

In recent decades, innovation and entrepreneurship have become buzz words. In reality, traditional research with empirical results is not practical for analyzing these newly launched projects of small and micro enterprises before production and sale. Actually, the future market prospect is an important criterion for evaluating entrepreneurial projects. However, this direction has not been well explored due to the limitations of scenarios and technical challenges especially for these small and micro enterprises. In this paper, we construct an interesting study of exploiting the market prospect from the sales markets (i.e., E-commerce) to help evaluate newly-posted campaigns in crowdfunding. Specifically, we propose a novel Market-oriented Prospect Analysis with Transferring Attention (MoPa-A) model which contains two learning modules, i.e., HostTask Learning and GuestTask Learning connected and enhanced by transferring attention. The former is designed for funding performance modeling with heterogeneous features of crowdfunding campaigns, and the latter is to represent and transfer the latent semantics of market prospect for target campaigns from campaigns comments with the help of relevant products in sales market. The model design of MoPa-A brings some new insights on flexible knowledge transfer for different or cross domains.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement. Existing approaches usually focus on assigning a set of events organized to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this work, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. We propose a method to calculate event feasible time period based on event time prediction, and design a greedy algorithm and two approximation algorithms to solve the PETA problem. Extensive experiments on both real and synthetic datasets demonstrate that the proposed algorithms have high effectiveness and efficiency.

As an essential task of talent recruitment, person-job fit aims to measure the matching degree between talent qualifi-cation and the job requirements of a position. Existing studies usually formulate this task as a long text matching problem with a focus on learning effective representations of both job postings and resumes. However, it is commonly known that there exists a semantic gap between textual job postings and textual resumes. Therefore, in this paper, we study how to improve person-job fit by bridging this semantic gap with the help of prior knowledge. To this end, we first design a distantly supervised skill extraction model to identify the skill entities from the given job postings and resumes using only unlabeled data and skill entity dictionaries. The identified skill entities will be used to construct a skill knowledge graph (KG) on the global corpus, which can provide the prior knowledge. Also, we propose a knowledge enhanced person-job fit approach for talent recruitment. Here, we model job postings and resumes as two graphs and fuse the prior external knowledge into the graph representation learning. Specifically, we first build the graphs from job posting and resume text. Then, we design a knowledge-aware graph encoder that can not only capture the contextual word relationships within each job posting or resume, but also incorporate the prior knowledge into node representation learning. In addition, we propose an interactive learning method to perform effective graph matching in both graph-level and node-level, respectively. Meanwhile, a multi-task learning strategy is introduced to facilitate the graph representation learning. Finally, extensive experiments conducted on real-world datasets have clearly validated the effectiveness of our approaches compared with state-of-the-art baselines.

Intercorporate investment has a large impact in financial performance and long-term development of a corporate. Among all the concerns for a company ’s investment strategy, complementary and substitutable investments are two fundamental factors. However, these two relations are implicit and entangled in the complex corporate network, requiring extra caution before investment. To this end, in this paper, we proposed a novel graph convolutional network called Series-Parallel decomposed Graph Convolutional Network (SPGCN). We first decompose the complementary and substitutable relations as two information propagating directions in company dependency graph, producing multifaceted node features. Then, with an Attentive Aggregation Module, we are able to further measure the impact of both features to the final investment decision making, producing an interpretable analysis for investment strategy. Finally, we conduct experiments on a real-world dataset, to show the effectiveness of decomposing two concerns on investment recommendation task. With visualization and case studies, our method also shows great potential to help understand and conduct complementary and substitutable investment decisions. We open source our code to support future research: https://github.com/lem0n1e/SPGCN.

Traffic flow imputation provides a more-complete view of traffic flows, and thus is a fundamental function in building Intelligent Transportation Systems. The performance of traffic flow imputation has a big impact on a wide range of downstream applications, such as traffic forecasting and control. Therefore, in this paper, we propose a Multi-grAph Convolutional Recurrent netwOrk (MACRO) framework for supporting fine-grained lane-level traffic flow imputation, which can help to reconstruct more complete traffic flows at the lane level. Specifically, we first design a spatial dependency module to model the diversified spatial correlations within traffic flows, where multi-relation graphs are first constructed to consider correlations from various perspective, then a multi-graph convolution neural network is proposed to capture the integrated spatial dependencies of traffic flows and adequately propagate the observed traffic values to mitigate data sparsity problem from spatial domain. Also, to handle the temporally continuous data missing issue, we adopt a modified bi-directional recurrent neural network to capture traffic flows’ temporal dependencies by considering both historical and future information, and employ a temporal decay mechanism to control the irregular information transfer between adjacent time slices. Moreover, a spatio-temporal knowledge integration module is devised to comprehensively integrate multi-resolution spatiotemporal knowledge for traffic flow imputation. Finally, extensive experiments on the real-world dataset demonstrate that the performance of MACRO outperforms several state-of-the-art baselines with respect to traffic flow imputation.

Electric Vehicles (EVs) have been emerging as a promising low-carbon transport target. While a large number of public charging stations are available, the use of these stations is often imbalanced, causing many problems to Charging Station Operators (CSOs). To this end, in this paper, we propose a Multi-Agent Graph Convolutional Reinforcement Learning (MAGC) framework to enable CSOs to achieve more effective use of these stations by providing dynamic pricing for each of the continuously arising charging requests with optimizing multiple long-term commercial goals. Specifically, we first formulate this charging station request-specific dynamic pricing problem as a mixed competitive-cooperative multi-agent reinforcement learning task, where each charging station is regarded as an agent. Moreover, by modeling the whole charging market as a dynamic heterogeneous graph, we devise a multi-view heterogeneous graph attention networks to integrate complex interplay between agents induced by their diversified relationships. Then, we propose a shared meta generator to generate individual customized dynamic pricing policies for large-scale yet diverse agents based on the extracted meta characteristics. Finally, we design a contrastive heterogeneous graph pooling representation module to learn a condensed yet effective state action representation to facilitate policy learning of large-scale agents. Extensive experiments on two real-world datasets demonstrate the effectiveness of MAGC and empirically show that the overall use of stations can be improved if all the charging stations in a charging market embrace our dynamic pricing policy.

Talent demand and supply forecasting aims to model the variation of the labor market, which is crucial to companies for recruitment strategy adjustment and to job seekers for proactive career path planning. However, existing approaches either focus on talent demand or supply forecasting, but overlook the interconnection between demand-supply sequences among different companies and positions. To this end, in this paper, we propose a Dynamic Heterogeneous Graph Enhanced Meta-learning (DH-GEM) framework for fine-grained talent demand-supply joint prediction. Specifically, we first propose a Demand-Supply Joint Encoder-Decoder (DSJED) and a Dynamic Company-Position Heterogeneous Graph Convolutional Network (DyCP-HGCN) to respectively capture the intrinsic correlation between demand and supply sequences and company-position pairs. Moreover, a Loss-Driven Sampling based Meta-learner (LDSM) is proposed to optimize long-tail forecasting tasks with a few training data. Extensive experiments have been conducted on three real-world datasets to demonstrate the effectiveness of our approach compared with five baselines. DH-GEM has been deployed as a core component of the intelligent human resource system of a cooperative partner.

Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society. Among them, the pairwise Origin-Destination (OD) demand prediction is a valuable but challenging problem due to several factors: (i) the large number of possible OD pairs, (ii) implicitness of spatial dependence, and (iii) complexity of traffic states. To address the above issues, this paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD). Firstly, a continuous-time dynamic graph representation learning framework is constructed, which maintains a dynamic state vector for each traffic node (metro stations or taxi zones). The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Secondly, a multi-level structure learning module is proposed to model the spatial dependency of station-level nodes. It can not only exploit relations between nodes adaptively from data, but also share messages and representations via cluster-level and area-level virtual nodes. Lastly, a cross-level fusion module is designed to integrate multi-level memories and generate comprehensive node representations for the final prediction. Extensive experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.

Recent studies have shown great promise in applying graph neural networks for multivariate time series forecasting, where the interactions of time series are described as a graph structure and the variables are represented as the graph nodes. Along this line, existing methods usually assume that the graph structure (or the adjacency matrix), which determines the aggregation manner of graph neural network, is fixed either by definition or self-learning. However, the interactions of variables can be dynamic and evolutionary in real-world scenarios. Furthermore, the interactions of time series are quite different if they are observed at different time scales. To equip the graph neural network with a flexible and practical graph structure, in this paper, we investigate how to model the evolutionary and multi-scale interactions of time series. In particular, we first provide a hierarchical graph structure cooperated with the dilated convolution to capture the scale-specific correlations among time series. Then, a series of adjacency matrices are constructed under a recurrent manner to represent the evolving correlations at each layer. Moreover, a unified neural network is provided to integrate the components above to get the final prediction. In this way, we can capture the pair-wise correlations and temporal dependency simultaneously. Finally, experiments on both single-step and multi-step forecasting tasks demonstrate the superiority of our method over the state-of-the-art approaches.

It is critical and important to detect anomalies in event sequences, which becomes widely available in many application domains. Indeed, various efforts have been made to capture abnormal patterns from event sequences through sequential pattern analysis or event representation learning. However, existing approaches usually ignore the semantic information of event content. To this end, in this paper, we propose a self-attentive encoder-decoder transformer framework, Content-Aware Transformer CAT, for anomaly detection in event sequences. In CAT, the encoder learns preamble event sequence representations with content awareness, and the decoder embeds sequences under detection into a latent space, where anomalies are distinguishable. Specifically, the event content is first fed to a content-awareness layer, generating representations of each event. The encoder accepts preamble event representation sequence, generating feature maps. In the decoder, an additional token is added at the beginning of the sequence under detection, denoting the sequence status. A one-class objective together with sequence reconstruction loss is collectively applied to train our framework under the label efficiency scheme. Furthermore, CAT is optimized under a scalable and efficient setting. Finally, extensive experiments on three real-world datasets demonstrate the superiority of CAT.

Online medical consultation, which enables patients to remotely inquire doctors in the form of web chatting, has become an indispensable part of the social health care system. Intuitively, it is a crucial step to recommend suitable doctor candidates for patients, especially with suffering the severe cold-start challenge of patients due to the limited historical records and insufficient description of patient condition. Along this line, in this paper, we propose a novel Dialogue based Doctor Recommendation (DDR) model, which comprehensively integrates three types of information in modeling, including the profile and chief complaint from patients, the historical records of doctors and the patient-doctor dialogue. Accordingly, we propose 1) a patient encoder which represents the patient's condition and medical requirements; 2) a doctor encoder which distills the doctor's expertise and communication skills; 3) a dialogue encoder which extracts textual features from doctor-patient conversation. Specifically, since the patient-doctor dialogue is not available in the testing stage, we propose to simulate the dialogue embedding with patient embedding via a contrastive learning based module. Experimental results on a real-world data set show that the proposed DDR model can outperform state-of-the-art recommendation-based methods. Moreover, considering the accessibility variance of online medical consultation services between the youth and the elderly, we also conduct a fairness study on the proposed DDR model.

Pre-travel out-of-town recommendation aims to recommend Point-of-Interests (POIs) to the users who plan to travel out of their hometown in the near future yet have not decided where to go, i.e., their destination regions and POIs both remain unknown. It is a non-trivial task since the searching space is vast, which may lead to distinct travel experiences in different out-of-town regions and eventually confuse decision-making. Besides, users' out-of-town travel behaviors are affected not only by their personalized preferences but heavily by others' travel behaviors. To this end, we propose a Crowd-Aware Pre-Travel Out-of-town Recommendation framework (CAPTOR) consisting of two major modules: spatial-affined conditional random field (SA-CRF) and crowd behavior memory network (CBMN). Specifically, SA-CRF captures the spatial affinity among POIs while preserving the inherent information of POIs. Then, CBMN is proposed to maintain the crowd travel behaviors w.r.t. each region through three affiliated blocks reading and writing the memory adaptively. We devise the elaborated metric space with a dynamic mapping mechanism, where the users and POIs are distinguishable both inherently and geographically. Extensive experiments on two real-world nationwide datasets validate the effectiveness of CAPTOR against the pre-travel out-of-town recommendation task.

With the prosperity of recommender systems, the biases existing in user behaviors, which may lead to inconsistency between user preference and behavior records, have attracted wide attention. Though large efforts have been made to infer user preference from biased data with learning to debias, unfortunately, they mainly focus on the effect of one specific item attribute, e.g., position or modality which may affect users’ click probability on items. However, the comprehensive description for potential interactions between multiple items with various attributes, namely the context bias between items, may not be fully summarized. To that end, in this paper, we design a novel Context Bias aware Recommendation (CBR) model for describing and debiasing the context bias caused by comprehensive interactions between multiple items. Specifically, we first propose a content encoder and a bias encoder based on multi-head self-attention to embed the latent interactions between items. Then, we calculate the biased representation for users based on an attention network, which will be further utilized to infer the negative preference, i.e., the dislikes of users based on the items the user never clicked. Finally, the real user preference will be captured based on the negative preference to estimate the click prediction score. Extensive experiments on a real-world dataset demonstrate the competitiveness of our CBR framework compared with state-of-the-art baseline methods.

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.

Data science competitions are becoming increasingly popular for enterprises collecting advanced innovative solutions and allowing contestants to sharpen their data science skills. Most existing studies about data science competitions have a focus on improving task-specific data science techniques, such as algorithm design and parameter tuning. However, little effort has been made to understand the data science competition itself. To this end, in this article, we shed light on the team’s competition performance, and investigate the team’s evolving performance in the crowd-sourcing competitive innovation context. Specifically, we first acquire and construct multi-sourced datasets of various data science competitions, including the KDD Cup 2019 machine learning competition and beyond. Then, we conduct an empirical analysis to identify and quantify a rich set of features that are significantly correlated with teams’ future performances. By leveraging team’s rank as a proxy, we observe “the stronger, the stronger” rule; that is, top-ranked teams tend to keep their advantages and dominate weaker teams for the rest of the competition. Our results also confirm that teams with diversified backgrounds tend to achieve better performances. After that, we formulate the team’s future rank prediction problem and propose the Multi-Task Representation Learning (MTRL) framework to model both static features and dynamic features. Extensive experimental results on four real-world data science competitions demonstrate the team’s future performance can be well predicted by using MTRL. Finally, we envision our study will not only help competition organizers to understand the competition in a better way, but also provide strategic implications to contestants, such as guiding the team formation and designing the submission strategy.

Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time. Traditional OSC methods usually train discriminative or generative models with the owned in-class data, and then utilize the pre-trained models to classify test data directly. However, these methods always suffer from the embedding confusion problem, i.e., partial out-of-class instances are mixed with in-class ones of similar semantics, making it difficult to classify. To solve this problem, we unify semi-supervised learning to develop a novel OSC algorithm, S2OSC, which incorporates out-of-class instances filtering and model re-training in a transductive manner. In detail, given a pool of newly coming test data, S2OSC firstly filters the mostly distinct out-of-class instances using the pre-trained model, and annotates super-class for them. Then, S2OSC trains a holistic classification model by combing in-class and out-of-class labeled data with the remaining unlabeled test data in a semi-supervised paradigm. Furthermore, considering that data are usually in the streaming form in real applications, we extend S2OSC into an incremental update framework (I-S2OSC), and adopt a knowledge memory regularization to mitigate the catastrophic forgetting problem in incremental update. Despite the simplicity of proposed models, the experimental results show that S2OSC achieves state-of-the-art performance across a variety of OSC tasks, including 85.4% of F1 on CIFAR-10 with only 300 pseudo-labels. We also demonstrate how S2OSC can be expanded to incremental OSC setting effectively with streaming data.

Labeling data (e.g., labeling the people, objects, actions, and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model’s output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of (1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and (2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints, respectively. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53% execution time without loss of any valuable labels.

The understanding of people’s inter-regional mobility behaviors, such as predicting the next activity region (AR) or uncovering the intentions for regional mobility, is of great value to public administration or business interests. While there are numerous studies on human mobility, these studies are mainly from a statistical view or study movement behaviors within a region. The work on individual-level inter-regional mobility behavior is limited. To this end, in this article, we propose a dynamic region-relation-aware graph neural network (DRRGNN) for exploring individual mobility behaviors over ARs. Specifically, we aim at developing models that can answer three questions: (1) Which regions are the ARs? (2) Which region will be the next AR, and (3) Why do people make this regional mobility? To achieve these tasks, we first propose a method to find out people’s ARs. Then, the designed model integrates a dynamic graph convolution network (DGCN) and a recurrent neural network (RNN) to depict the evolution of relations between ARs and mine the regional mobility patterns. In the learning process, the model further considers peoples’ profiles and visited point-of-interest (POIs). Finally, extensive experiments on two real-world datasets show that the proposed model can significantly improve accuracy for both the next AR prediction and mobility intention prediction.

Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility. However, the flexibility of these dockless systems comes at the cost of management and operation complexity. Indeed, the imbalanced and dynamic use of bikes leads to mandatory rebalancing operations, which impose a critical need for effective bike traffic flow prediction. While efforts have been made in developing traffic flow prediction models, existing approaches lack interpretability, and thus have limited value in practical deployment. To this end, we propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. Specifically, by dividing the urban area into regions according to flow density, we first model the spatio-temporal bike flows between regions with graph regularized sparse representation, where graph Laplacian is used as a smooth operator to preserve the commonalities of the periodic data structure. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Moreover, the bike flows can be predicted with the interpretable base matrices and learned parameters. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems. In addition, the interpretability of our flow pattern exploitation is further illustrated through a case study where IBFP provides valuable insights into bike flow analysis.

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.

Transportation recommendation is one important map service in navigation applications. Previous transportation recommendation solutions fail to deliver satisfactory user experience because their recommendations only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and largely overlook situational context. In this work, we propose$\mathsf {Hydra}$, a multi-task deep learning based recommendation system that offers multi-modal transportation planning and is adaptive to various situational context (e.g., nearby point-of-interest (POI) distribution and weather). We leverage the availability of existing routing engines and big urban data, and design a novel two-level framework that integrates uni-modal and multi-modal (e.g., taxi-bus, bus-cycle) routes as well as heterogeneous urban data for intelligent multi-modal transportation recommendation. In addition to urban context features constructed from multi-source urban data, we learn the latent representations of users, origin-destination (OD) pairs and transportation modes based on user implicit feedbacks, which captures the collaborative transportation mode preferences of users and OD pairs. Moreover, we propose two models to recommend the proper route among various uni-modal and multi-modal transportation routes: (1) a light-weight gradient boosting decision tree (GBDT) based recommendation model; and (2) a multi-task wide and deep learning (MTWDL) based recommendation model. We also optimize the framework to support real-time, large-scale route query and recommendation. We deploy$\mathsf {Hydra}$on Baidu Maps,11.https://maps.baidu.com/.one of the world's largest map services. Real-world urban-scale experiments demonstrate the effectiveness and efficiency of our proposed system. Since its deployment in August 2018,$\mathsf {Hydra}$has answered over a hundred million route recommendation queries made by over ten million distinct users. The GBDT based model and MTWDL based model achieve 82.8 and 96.6 percent relative improvement of user click ratio, respectively.

Rating prediction is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially graph neural networks, have made impressive progress on this problem. Despite their effectiveness, existing methods focus on modeling the user-item interaction graph. The inherent drawback of such methods is that their performance is bound to the density of the interactions, which is however usually of high sparsity. More importantly, for a strict cold start user/item that neither appears in the training data nor has any interactions in the test stage, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph. In this work, we develop a novel frameworkAttribute Graph Neural Networks(AGNN) by exploiting the attribute graph rather than the commonly used interaction graph. This leads to the capability of learning embeddings for the strict cold start users/items. Our AGNN can produce the preference embedding for a strict cold user/item by learning on the distribution of attributes with an extended variational auto-encoder (eVAE) structure. Moreover, we propose a new graph neural network variant, i.e., gated-GNN, to effectively aggregate various attributes of different modalities in a neighborhood. Empirical results on three real-world datasets demonstrate that our model yields significant improvements for strict cold start recommendations and outperforms or matches the state-of-the-art performance in the warm start scenario.

In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose to threaten the retailers for illegal profits. PMUs are difficult to be detected because they utilize masking strategies to disguise themselves as normal users. Specifically, there are three challenges for PMU detection: 1) professional malicious users do not conduct any abnormal or illegal interactions (they never concurrently leave too many negative reviews and low ratings at the same time), and they conduct masking strategies to disguise themselves. Therefore, conventional outlier detection methods are confused by their masking strategies. 2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem. 3) there are no datasets with labels for professional malicious users in public, which makes PMU detection an unsupervised learning problem. To this end, we propose an unsupervised multi-modal learning model: MMD, which employs Metric learning for professional Malicious users Detection with both ratings and reviews. MMD first utilizes a modified RNN to project the informational review into a sentiment score, which jointly considers the ratings and reviews. Then professional malicious user profiling (MUP) is proposed to catch the sentiment gap between sentiment scores and ratings. MUP filters the users and builds a candidate PMU set. We apply a metric learning-based clustering to learn a proper metric matrix for PMU detection. Finally, we can utilize this metric and labeled users to detect PMUs. Specifically, we apply the attention mechanism in metric learning to improve the model’s performance. The extensive experiments in four datasets demonstrate that our proposed method can solve this unsupervised detection problem. Moreover, the performance of the state-of-the-art recommender models is enhanced by taking MMD as a preprocessing stage.

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. The effective prediction of city-wide parking availability can boost parking efficiency, improve urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for city-wide parking availability prediction because of three major challenges: 1) the non-euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and bluetooth sensor). To this end, we propose aSemi-supervisedHierarchicalRecurrent Graph Neural Network-X(SHARE-X) to predict parking availability of each parking lot within a city. Specifically, we first propose a hierarchical graph convolution module to model the non-euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a multi-resolution soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Moreover, we devise a hierarchical attentive recurrent network module to incorporate both short and long-term dynamic temporal dependencies of parking lots. Additionally, a parking availability approximation module is introduced to estimate missing real-time parking availabilities from both spatial and temporal domains. Finally, experiments on two real-world datasets demonstrate thatSHARE-Xoutperforms eight state-of-the-art baselines in parking availability prediction.

Competition between Points of Interest (POIs) refers to the situation in which two POIs directly or indirectly provide similar services to secure businesses. A large portion of prior studies on competition analysis focuses on mining textual data, e.g., news articles and social comments. However, the increasing availability of human mobility and mobile query data enables a new paradigm for analyzing the competitive relationships among POIs, which remains largely unexplored. To this end, in this paper, we attempt to mine large-scale online map search query data for better understanding POI competitive relationships. Based on a co-query POI graph built from the map search query data, we develop a novel neural graphlet-based prediction framework to predict the competitive relationships among POIs. A unique perspective of our model is to infer latent POI competitive relationships by integrating multiple distinct factors, e.g., graphlet structure, geographical distance, and regional features, reflected in map search query data and POI data. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness of the proposed framework, and show that our framework outperforms all baselines with a significant margin in all evaluation metrics.

The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this article, we propose three novel approaches to intelligent job interview by learning the large-scale real-world interview data. Specifically, we first develop a preliminary model, named Joint Learning Model on Interview Assessment (JLMIA), to mine the relationship among job description, candidate resume, and interview assessment. Then, we further design an enhanced model, named Neural-JLMIA, to improve the representative capability by applying neural variance inference. Last, we propose to refine JLMIA with Refined-JLMIA (R-JLMIA) by modeling individual characteristics for each collection, i.e., disentangling the core competences from resume and capturing the evolution of the semantic topics over different interview rounds. As a result, our approaches can effectively learn the representative perspectives of different job interview processes from the successful job interview records in history. In addition, we exploit our approaches for two real-world applications, i.e., person-job fit and skill recommendation for interview assessment. Extensive experiments conducted on real-world data clearly validate the effectiveness of our models, which can lead to substantially less bias in job interviews and provide an interpretable understanding of job interview assessment.

As a major component of strategic talent management, learning and development (L&D) aims at improving the individual and organization performances through planning tailored training for employees to increase and improve their skills and knowledge. While many companies have developed the learning management systems (LMSs) for facilitating the online training of employees, a long-standing important issue is how to achieve personalized training recommendations with the consideration of their needs for future career development. To this end, in this article, we present a focused study on the explainable personalized online course recommender system for enhancing employee training and development. Specifically, we first propose a novel end-to-end hierarchical framework, namely Demand-aware Collaborative Bayesian Variational Network (DCBVN), to jointly model both the employees’ current competencies and their career development preferences in an explainable way. In DCBVN, we first extract the latent interpretable representations of the employees’ competencies from their skill profiles with autoencoding variational inference based topic modeling. Then, we develop an effective demand recognition mechanism for learning the personal demands of career development for employees. In particular, all the above processes are integrated into a unified Bayesian inference view for obtaining both accurate and explainable recommendations. Furthermore, for handling the employees with sparse or missing skill profiles, we develop an improved version of DCBVN, called the Demand-aware Collaborative Competency Attentive Network (DCCAN) framework , by considering the connectivity among employees. In DCCAN, we first build two employee competency graphs from learning and working aspects. Then, we design a graph-attentive network and a multi-head integration mechanism to infer one’s competency information from her neighborhood employees. Finally, we can generate explainable recommendation results based on the competency representations. Extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of both of our frameworks, as well as their robustness on sparse and cold-start scenarios.

A session-based recommender system (SBRS) captures users’ evolving behaviors and recommends the next item by profiling users in terms of items in a session. User intent and user preference are two factors affecting his (her) decisions. Specifically, the former narrows the selection scope to some item types, while the latter helps to compare items of the same type. Most SBRSs assume one arbitrary user intent dominates a session when making a recommendation. However, this oversimplifies the reality that a session may involve multiple types of items conforming to different intents. In current SBRSs, items conforming to different user intents have cross-interference in profiling users for whom only one user intent is considered. Explicitly identifying and differentiating items conforming to various user intents can address this issue and model rich contextual information of a session. To this end, we design a framework modeling user intent and preference explicitly, which empowers the two factors to play their distinctive roles. Accordingly, we propose a key-array memory network (KA-MemNN) with a hierarchical intent tree to model coarse-to-fine user intents. The two-layer weighting unit (TLWU) in KA-MemNN detects user intents and generates intent-specific user profiles. Furthermore, the hierarchical semantic component (HSC) integrates multiple sets of intent-specific user profiles along with different user intent distributions to model a multi-intent user profile. The experimental results on real-world datasets demonstrate the superiority of KA-MemNN over selected state-of-the-art methods.

As a fundamental task of document content analysis, keyphrase extraction (KE) aims at predicting a set of lexical units that conveys the core information of the document. In this paper, we study the problem of KE in the talent recruitment. This problem is critical for the development of a variety of intelligent recruitment services, such as person-job fit, market trend analysis and course recommendation. However, unlike traditional textual data, the texts from the recruitment domain, such as resume and job postings, often have unique characteristics of abbreviation and succinctness, resulting in massive keyphrases consisting of inconsecutive words that are hard to be fully captured by existing KE methods. To this end, we propose an interactive neural network approach, INKE, for facilitating KE in the talent recruitment. To be specific, we first introduce a novel keyphrase indicator that captures the explicit hint information for each keyphrase. Then, we design a dynamically-initialized decoder which can generate keyphrases in an interactive manner. Moreover, we propose a hierarchical reinforcement learning algorithm to enhance the interaction between the hint information capture and keyphrase generation. Finally, extensive experiments on real-world data clearly validate the effectiveness and interpretability of INKE compared with state-of-the-art baselines.

Description of Points of Interest (POIs) plays an important role to enhance the quality of many location-based services, such as displaying concentrated information of POIs for user-friendly experience and leading to successful POI recommendation. However, only a few popular POIs have enough description on the web. Collecting or writing high-quality descriptions for many unpopular or long-tail POIs remains a huge challenge for online map services, especially considering there are numerous new appeared POIs every day. Unlike existing studies about automatic product description generation, the POI description is quite diverse across different locations over a country, and requires high expert knowledge. To address this issue, we first study the POI description generation problem by proposing a novel model, named as Multi Mode Description Generator (MMDG), to automatically generate description based on POIs' reviews and other features. To extract key information for POI description generation, MMDG is equipped with a multi-mode encoder and a transformer-based decoder. Besides user reviews, the multi-mode encoder also considers the category and spatial context information of target POIs, and integrate them with a fusion function. We have conducted an extensive experimental evaluation on a large-scale real-world dataset to demonstrate its effectiveness and superiority over state-of-the-art baselines in terms of various metrics.

The fairness issue is very important in deploying machine learning models as algorithms widely used in human society can be easily in discrimination. Researchers have studied disparity on tabular data a lot and proposed many methods to relieve bias. However, studies towards unfairness in graph are still at early stage while graph data that often represent connections among people in real-world applications can easily give rise to fairness issues and thus should be attached to great importance. Fair representation learning is one of the most effective methods to relieve bias, which aims to generate hidden representations of input data while obfuscating sensitive information. In graph setting, learning fair representations of graph (also called fair graph embeddings) is effective to solve graph unfairness problems. However, most existing works of fair graph embeddings only study fairness in a coarse granularity (i.e., group fairness), but overlook individual fairness. In this paper, we study fair graph representations from different levels. Specifically, we consider both group fairness and individual fairness on graph. To debias graph embeddings, we propose FairGAE, a fair graph auto-encoder model, to derive unbiased graph embeddings based on the tailor-designed fair Graph Convolution Network (GCN) layers. Then, to achieve multi-level fairness, we design a Wasserstein distance based regularizer to learn the optimal transport for fairer embeddings. To overcome the efficiency concern, we further bring up Sinkhorn divergence as the approximations of Wasserstein cost for computation. Finally, we apply the learned unbiased embeddings into the node classification task and conduct extensive experiments on two real-world graph datasets to demonstrate the improved performances of our approach.

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 2021 International Workshop on Talent and Management Computing, is to bring together researchers and practitioners to discuss both the critical problems faced by talent and management related domains, and potential data-driven solutions by leveraging state-of-the-art data mining technologies.

Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e.g., real estate agents, appraisers, lenders, and buyers). However, it is a non-trivial task for accurate real estate appraisal because of three major challenges: (1) The complicated influencing factors for property value; (2) The asynchronously spatiotemporal dependencies among real estate transactions; (3) The diversified correlations between residential communities. To this end, we propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. Specifically, by acquiring and integrating multi-source urban data, we first construct a rich feature set to profile the real estate from multiple perspectives~(e.g., geographical distribution, human mobility distribution, and resident demographics distribution). Then, an evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronously spatiotemporal dependencies among real estate transactions. Moreover, to further incorporate valuable knowledge from the view of residential communities, we devise a hierarchical heterogeneous community graph convolution module to capture diversified correlations between residential communities. Finally, an urban district partitioned multi-task learning module is introduced to generate differently distributed value opinions for real estate. Extensive experiments on two real-world datasets demonstrate the effectiveness of MugRep and its components and features.

Dynamic Graph Neural Networks (DGNNs) have become one of the most promising methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed forecasting, existing approaches are usually built on a static adjacency matrix (no matter predefined or self-learned) to learn spatial relationships among different road segments, even if the impact of two road segments can be changeable dynamically during a day. Moreover, the future traffic speed cannot only be related with the current traffic speed, but also be affected by other factors such as traffic volumes. To this end, in this paper, we aim to explore these dynamic and multi-faceted spatio-temporal characteristics inherent in traffic data for further unleashing the power of DGNNs for better traffic speed forecasting. Specifically, we design a dynamic graph construction method to learn the time-specific spatial dependencies of road segments. Then, a dynamic graph convolution module is proposed to aggregate hidden states of neighbor nodes to focal nodes by message passing on the dynamic adjacency matrices. Moreover, a multi-faceted fusion module is provided to incorporate the auxiliary hidden states learned from traffic volumes with the primary hidden states learned from traffic speeds. Finally, experimental results on real-world data demonstrate that our method can not only achieve the state-of-the-art prediction performances, but also obtain the explicit and interpretable dynamic spatial relationships of road segments.

In knowledge graphs, there are usually different types of nodes, multiple heterogeneous relations, and numerous attributes of nodes and edges, which impose the challenges on the task of Node Importance Estimation (NIE). Indeed, existing NIE approaches, such as PageRank (PR) and Node-Degree (ND), are not designed for handling knowledge graphs with the rich information related with these multifarious nodes and edges. To this end, in this paper, we propose a representation learning framework to leverage the rich information inherent in these multifarious nodes and edges for improving node importance estimation in knowledge graphs. Specifically, we provide a Relational Graph Transformer Network (RGTN), where a relational graph transformer is first proposed to propagate node information with the consideration of semantic predicate representations. Here, the assumption is that different predicates may have distinct effects on the transmission of node importance. Then, two separate encoders are designed to capture both the structural and semantic information of nodes respectively, and a co-attention module is developed to fuse the two separate representations of nodes. Next, an attention-based aggregation module is adopted to map the representations of nodes to their importance values. In addition, a learning-to-rank loss is designed to ensure that the learned representations can be aware of the relative ranking information among nodes. Finally, extensive experiments have been conducted on real-world knowledge graphs, and the results illustrate that our model outperforms the existing methods consistently for all the evaluation metrics. The code and the data are available at https://github.com/GRAPH-0/RGTN-NIE.

Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. However, existing solutions usually treat protein-ligand complexes as topological graph data, thus the biomolecular structural information is not fully utilized. The essential long-range interactions among atoms are also neglected in GNN models. To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool). Specifically, PGAL iteratively performs the node-edge aggregation process to update embeddings of nodes and edges while preserving the distance and angle information among atoms. Then, PiPool is adopted to gather interactive edges with a subsequent reconstruction loss to reflect the global interactions. Exhaustive experimental study on two benchmarks verifies the superiority of SIGN.

Medical entity relation verification is a crucial step to build a practical and enterprise medical knowledge graph (MKG) because high-precision medical entity relation is a key requirement for many MKG-based applications. Existing relation verification approaches for general knowledge graphs are not designed for considering medical domain knowledge, although it is central to achieve high-quality entity relation verification for MKG. To this end, in this paper, we introduce a system for medical entity relation verification with large-scale machine reading comprehension. The proposed system is tailored to overcome the unique challenges of medical relation verification including high variants of medical terms, the high difficulty of evidence searching in complex medical documents, and the lack of evidence labels for supervision. To deal with the problem of variants of medical terms, we introduce a synonym-aware retrieve model to retrieve the potential evidence implicitly verifying the given claim. To better utilize the medical domain knowledge, a relation-aware evidence detector and a medical ontology-enhanced aggregator are developed to improve the performance of the relation verification module. Moreover, to overcome the challenge of providing high-quality evidence due to the lack of labels, we introduce an interactive collaborative-training method to iteratively improve the evidence accuracy. Finally, we conduct extensive experiments to demonstrate that the performance of our proposed system is superior to all comparable models. We also demonstrate that our system can significantly reduce the annotation time by medical experts in real-world verification tasks. It can help to improve the efficiency by nearly 300%. In particular, our system has been embedded into the Baidu Clinical Decision Support System.

Motivated by the success of pre-trained language models such as BERT in a broad range of natural language processing (NLP) tasks, recent research efforts have been made for adapting these models for different application domains. Along this line, existing domain-oriented models have primarily followed the vanilla BERT architecture and have a straightforward use of the domain corpus. However, domain-oriented tasks usually require accurate understanding of domain phrases, and such fine-grained phrase-level knowledge is hard to be captured by existing pre-training scheme. Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge. But meanwhile, there is a risk of introducing noise due to the lack of groundtruth word-level alignment. To address the issues, we provide a generalized domain-oriented approach, which leverages auxiliary domain knowledge to improve the existing pre-training framework from two aspects. First, to preserve phrase knowledge effectively, we build a domain phrase pool as auxiliary knowledge, meanwhile we introduce Adaptive Hybrid Masked Model to incorporate such knowledge. It integrates two learning modes, word learning and phrase learning, and allows them to switch between each other. Second, we introduce Cross Entity Alignment to leverage entity association as weak supervision to augment the semantic learning of pre-trained models. To alleviate the potential noise in this process, we introduce an interpretableOptimal Transport based approach to guide alignment learning. Experiments on four domain-oriented tasks demonstrate the superiority of our framework.

To cope with the fast-evolving business trend, it becomes critical for companies to continuously review their talent recruitment strategies by the timely forecast of talent demand in recruitment market. While many efforts have been made on recruitment market analysis, due to the sparsity of fine-grained talent demand time series and the complex temporal correlation of the recruitment market, there is still no effective approach for fine-grained talent demand forecast, which can quantitatively model the dynamics of the recruitment market. To this end, in this paper, we propose a data-driven neural sequential approach, namely Talent Demand Attention Network (TDAN), for forecasting fine-grained talent demand in the recruitment market. Specifically, we first propose to augment the univariate time series of talent demand at multiple grained levels and extract intrinsic attributes of both companies and job positions with matrix factorization techniques. Then, we design a Mixed Input Attention module to capture company trends and industry trends to alleviate the sparsity of fine-grained talent demand. Meanwhile, we design a Relation Temporal Attention module for modeling the complex temporal correlation that changes with the company and position. Finally, extensive experiments on a real-world recruitment dataset clearly validate the effectiveness of our approach for fine-grained talent demand forecast, as well as its interpretability for modeling recruitment trends. In particular, TDAN has been deployed as an important functional component of intelligent recruitment system of cooperative partner.

It is critical and important for venture investors to find high-potential startups at their early stages. Indeed, many efforts have been made to study the key factors for the success of startups through the topological analysis of the heterogeneous information network of people, startup, and venture firms or representation learning of latent startup profile features. However, the existing topological analysis lacks an in-depth understanding of heterogeneous information. Also, the approach based on representation learning heavily relies on domain-specific knowledge for feature selections. Instead, in this paper, we propose aScalable Heterogeneous Graph Markov Neural Network (SHGMNN) for identifying the high-potential startups. The general idea is to use graph neural networks (GNN) to learn effective startup representations through end-to-end efficient training and model the label dependency among startups through Maximum A Posterior (MAP) inference. Specifically, we first define different metapaths to capture various semantics over the heterogeneous information network (HIN) and aggregate all semantic information into a summated graph structure. To predict the high-potential early-stage startups, we introduce GNN to diffuse the information over the summated graph. We then adopt an MAP inference over Hinge-Loss Markov Random Fields to enforce label dependency. Here, a pseudolikelihood variational expectation-maximization (EM) framework is incorporated to optimize both MAP inference and GNN iteratively: The E-step calculates the inference, and the M-step updates the GNN. For efficiency concerns, we develop a GNN with a lightweight linear diffusion architecture to perform graph propagation over web-scale heterogeneous information networks. Finally, extensive experiments and case studies on real-world datasets demonstrate the superiority of SHGMNN.

Job mobility prediction is an emerging research topic that can benefit both organizations and talents in various ways, such as job recommendation, talent recruitment, and career planning. Nevertheless, most existing studies only focus on modeling the individual-level career trajectories of talents, while the impact of macro-level job transition relationships (e.g., talent flow among companies and job positions) has been largely neglected. To this end, in this paper we propose an enhanced approach to job mobility prediction based on a heterogeneous company-position network constructed from the massive career trajectory data. Specifically, we design an Attentive heterogeneous graph embedding for sequential prediction (Ahead) framework to predict the next career move of talents, which contains two components, namely an attentive heterogeneous graph embedding (AHGN) model and a Dual-GRU model for career path mining. In particular, the AHGN model is used to learn the comprehensive representation for company and position on the heterogeneous network, in which two kinds of aggregators are employed to aggregate the information from external and internal neighbors for a node. Afterwards, a novel type-attention mechanism is designed to automatically fuse the information of the two aggregators for updating node representations. Moreover, the Dual-GRU model is devised to model the parallel sequences that appear in pair, which can be used to capture the sequential interactive information between companies and positions. Finally, we conduct extensive experiments on a real-world dataset for evaluating our Ahead framework. The experimental results clearly validate the effectiveness of our approach compared with the state-of-the-art baselines in terms of job mobility prediction.

The contexts and cultures have a direct impact on student learning by affecting student's implicit cognitive states, such as the preference and the proficiency on specific knowledge. Motivated by the success of context-aware modeling in various fields, such as recommender systems, in this paper, we propose to study how to model context-aware features and adapt them for more precisely diagnosing student's knowledge proficiency. Specifically, by analyzing the characteristics of educational contexts, we design a two-stage framework ECD (Educational context-aware Cognitive Diagnosis), where a hierarchical attentive network is first proposed to represent the context impact on students and then an adaptive optimization is used to achieve diagnosis enhancement by aggregating the cognitive states reflected from both educational contexts and students' historical learning records. Moreover, we give three implementations of general ECD framework following the typical cognitive diagnosis solutions. Finally, we conduct extensive experiments on nearly 52 million records of the students sampled by PISA (Programme for International Student Assessment) from 73 countries and regions. The experimental results not only prove that ECD is more effective in student performance prediction since it can well capture the impact from educational contexts to students' cognitive states, but also give some interesting discoveries regarding the difference among different educational contexts in different countries and regions.

Nowadays, as organizations operate in very fast-paced and competitive environments, workforce has to be agile and adaptable to regularly learning new job skills. However, it is nontrivial for talents to know which skills to develop at each working stage. To this end, in this paper, we aim to develop a cost-effective recommendation system based on deep reinforcement learning, which can provide personalized and interpretable job skill recommendation for each talent. Specifically, we first design an environment to estimate the utilities of skill learning by mining the massive job advertisement data, which includes a skill-matching-based salary estimator and a frequent itemset-based learning difficulty estimator. Based on the environment, we design a Skill Recommendation Deep Q-Network (SRDQN) with multi-task structure to estimate the long-term skill learning utilities. In particular, SRDQN recommends job skills in a personalized and cost-effective manner; that is, the talents will only learn the recommended necessary skills for achieving their career goals. Finally, extensive experiments on a real-world dataset clearly validate the effectiveness and interpretability of our approach.

In today’s fast-evolving job market, the timely and effective understanding of the career trajectories of talents can help them quickly develop necessary skills and make the right career transitions at the right time. However, it is a non-trivial task for developing a successful career trajectory prediction method, which should have the abilities for finding the right timing for job-hopping, identifying the right companies, and matching the right positions for the candidates. While people have been trying to develop solutions for providing some of the above abilities, there is no total solution or complete framework to integrate all these abilities together. To this end, in this paper, we propose a unified time-aware career trajectory prediction framework, namely TACTP, which is capable of jointly providing the above three abilities for better understanding the career trajectories of talents. Along this line, we first exploit a hierarchical deep sequential modeling network for career embedding and extract latent talent factors from multiple networks, which are designed with different functions of handling related issues of the timing, companies, and positions for job-hopping. Then, we perform collaborative filtering for generating personalized predictions. Furthermore, we propose a temporal encoding mechanism to handle dynamic temporal information so that TACTP is capable of generating time-aware predictions by addressing the challenges for variable interval time sequence modeling. Finally, we have conducted extensive experiments on large-scale real-world data to evaluate TACTP against the state-of-the-art baselines, and the results show that TACTP has advantages over baselines on all targeted tasks for career trajectory prediction.

Query and Point-of-Interest (POI) matching, aiming at recommending the most relevant POIs from partial query keywords, has become one of the most essential functions in online navigation and ride-hailing applications. Existing methods for query-POI matching, such as Google Maps and Uber, have a natural focus on measuring the static semantic similarity between contextual information of queries and geographical information of POIs. However, it remains challenging for dynamic and personalized online query-POI matching because of the non-stationary and situational context-dependent query-POI relevance. Moreover, the large volume of online queries requires an adaptive and incremental model training strategy that is efficient and scalable in the online scenario. To this end, in this paper, we propose an Incremental Spatio-Temporal Graph Learning (IncreSTGL) framework for intelligent online query-POI matching. Specifically, we first model dynamic query-POI interactions as microscopic and macroscopic graphs. Then, we propose an incremental graph representation learning module to refine and update query-POI interaction graphs in an online incremental fashion, which includes: (i) a contextual graph attention operation quantifying query-POI correlation based on historical queries under dynamic situational context, (ii) a graph discrimination operation capturing the sequential query-POI relevance drift from a holistic view of personalized preference and social homophily, and (iii) a multi-level temporal attention operation summarizing the temporal variations of query-POI interaction graphs for subsequent query-POI matching. Finally, we introduce a lightweight semantic matching module for online query-POI similarity measurement. To demonstrate the effectiveness and efficiency of the proposed algorithm, we conduct extensive experiments on two real-world datasets collected from a leading online navigation and map service provider in China.

Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find the proper spots for charging, because of the limited charging infrastructures and the spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve the charging experience from various aspects over a long-term horizon. In this paper, we propose a framework, named Multi-Agent Spatio-Temporal Reinforcement Learning (Master), for intelligently recommending public accessible charging stations by jointly considering various long-term spatiotemporal factors. Specifically, by regarding each charging station as an individual agent, we formulate this problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with the centralized attentive critic to coordinate the recommendation between geo-distributed agents. Moreover, to quantify the influence of future potential charging competition, we introduce a delayed access strategy to exploit the knowledge of future charging competition during training. After that, to effectively optimize multiple learning objectives, we extend the centralized attentive critic to multi-critics and develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction. Finally, extensive experiments on two real-world datasets demonstrate that Master achieves the best comprehensive performance compared with nine baseline approaches.

Community search aims at finding densely connected subgraphs for query vertices in a graph. While this task has been studied widely in the literature, most of the existing works only focus on finding homogeneous communities rather than heterogeneous communities with different labels. In this paper, we motivate a new problem of cross-group community search, namely Butterfly-Core Community (BCC), over a labeled graph, where each vertex has a label indicating its properties and an edge between two vertices indicates their cross relationship. Specifically, for two query vertices with different labels, we aim to find a densely connected cross community that contains two query vertices and consists of butterfly networks, where each wing of the butterflies is induced by a k-core search based on one query vertex and two wings are connected by these butterflies. We first develop a heuristic algorithm achieving 2-approximation to the optimal solution. Furthermore, we design fast techniques of query distance computations, leader pair identifications, and index-based BCC local explorations. Extensive experiments on seven real datasets and four useful case studies validate the effectiveness and efficiency of our BCC and its multi-labeled extension models.

Because of strict response-time constraints, efficiency of top-k recommendation is crucial for real-world recommender systems. Locality sensitive hashing and index-based methods usually store both index data and item feature vectors in main memory, so they handle a limited number of items. Hashing-based recommendation methods enjoy low memory cost and fast retrieval of items, but suffer from large accuracy degradation. In this paper, we propose product Quantized Collaborative Filtering (pQCF) for better trade-off between efficiency and accuracy. pQCF decomposes a joint latent space of users and items into a Cartesian product of low-dimensional subspaces, and learns clustered representation within each subspace. A latent factor is then represented by a short code, which is composed of subspace cluster indexes. A user's preference for an item can be efficiently calculated via table lookup. We then develop block coordinate descent for efficient optimization and reveal the learning of latent factors is seamlessly integrated with quantization. We further investigate an asymmetric pQCF, dubbed as QCF, where user latent factors are not quantized and shared across different subspaces. The extensive experiments with 6 real-world datasets show that pQCF significantly outperforms the state-of-the-art hashing-based CF and QCF increases recommendation accuracy compared to pQCF.

For offering proactive services (e.g., personalized exercise recommendation) to the students in computer supported intelligent education, one of the fundamental tasks is predicting student performance (e.g., scores) on future exercises, where it is necessary to track the change of each student's knowledge acquisition during her exercising activities. Unfortunately, to the best of our knowledge, existing approaches can only exploit the exercising records of students, and the problem of extracting rich information existed in the materials (e.g., knowledge concepts, exercise content) of exercises to achieve both more precise prediction of student performance and more interpretable analysis of knowledge acquisition remains underexplored. To this end, in this paper, we present a holistic study of student performance prediction. To directly achieve the primary goal of performance prediction, we first propose a general Exercise-Enhanced Recurrent Neural Network (EERNN) framework by exploring both student's exercising records and the text content of corresponding exercises. In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise from its content. For making final predictions, we design two implementations on the basis of EERNN with different prediction strategies, i.e., EERNNM with Markov property and EERNNA with Attention mechanism. Then, to explicitly track student's knowledge acquisition on multiple knowledge concepts, we extend EERNN to an explainable Exercise-aware Knowledge Tracing (EKT) framework by incorporating the knowledge concept information, where the student's integrated state vector is now extended to a knowledge state matrix. In EKT, we further develop a memory network for quantifying how much each exercise can affect the mastery of students on multiple knowledge concepts during the exercising process. Finally, we conduct extensive experiments and evaluate both EERNN and EKT frameworks on a large-scale real-world data. The results in both general and cold-start scenarios clearly demonstrate the effectiveness of two frameworks in student performance prediction as well as the superior interpretability of EKT.

As a data-driven healthcare service, automatic treatment regimen design has great potential to improve healthcare efficiency and quality. However, it is a nontrivial endeavor to develop such a healthcare service due to two major challenges: 1) the treatment records are complex data objects consisting of various semantic and temporal information, and 2) the treatment outcome usually depends on a large number of internal and external factors. Because of these difficulties, automatic treatment regimen design is still an open research problem nowadays. To fill this research gap, this paper first formulates a treatment sequence as temporal sets, then provides a novel Extended Jaccard Similarity (EJS) measure to quantify the similarities between treatment sequences. We show that the proposed EJS is a general and effective measure to capture the similarity between two complex temporal sets. Further, we develop an efficient clustering algorithm which can achieve reasonable clustering results with only a portion of the pairwise similarities between treatment sequences and then extract a semantic prototype of the treatment regimen from each cluster of treatment sequences. Finally, we adopt a matrix factorization framework to predict the treatment outcomes by integrating multiple internal and external factors. We conduct comprehensive experiments on Electronic Medical Records (EMRs) of more than 28,000 patients from 14 hospitals. The results demonstrate the effectiveness of our approach and its superiority over the state-of-the-art ones.

In this paper, we propose a novel Semi-supervised Learning with Incomplete Modality (SLIM) method considering the modal consistency and complementarity simultaneously, and Kernel SLIM (SLIM-K) based on matrix completion for further solving the modal incompleteness. As is well known, most realistic data have multi-modal representations, multi-modal learning refers to the process of learning a precise model for complete modalities. However, due to the failures of data collection, self-deficiencies, or other various reasons, multi-modal examples are usually with incomplete modalities, which generate utility obstacle using previous methods. In this paper, SLIM integrates the intrinsic consistency and extrinsic complementary information for prediction and cluster simultaneously. In detail, SLIM forms different modal classifiers and clustering learner consistently in a unified framework, while using the extrinsic complementary information from unlabeled data against the insufficiencies brought by the incomplete modal issue. Moreover, in order to deal with missing modality in essence, we propose the SLIM-K, which takes the complemented kernel matrix into the classifiers and the cluster learner respectively. Thus, SLIM-K can solve the defects of missing modality in result. Finally, we give the discussion of generalization of incomplete modalities. Experiments on 13 benchmark multi-modal datasets and two real-world incomplete multi-modal datasets validate the effectiveness of our methods.

Recently accumulated massive amounts of geo-tagged photos provide an excellent opportunity to understand human behaviors and can be used for personalized tour recommendation. However, no existing work has considered the visual content information in these photos for tour recommendation. We believe the visual features of photos provide valuable information on measuring user / Point-of-Interest (POI) similarities, which is challenging due to data sparsity. To this end, in this paper, we propose a visual feature enhanced tour recommender system, named ‘Photo2Trip’, to utilize the visual contents and collaborative filtering models for recommendation. Specifically, we propose a Visual-enhanced Probabilistic Matrix Factorization model (VPMF), which integrates visual features into the collaborative filtering model, to learn user interests by leveraging the historical travel records. We then extend VPMF to End-to-End training framework to incorporate users (POIs) latent factors into the learning process of the visual content of photos, which generalizes the applicability of the proposed VPMF framework in tour recommendation. Extensive empirical studies verify that our proposed visual-enhanced personalized tour recommendation method outperforms other benchmark methods in terms of recommendation accuracy. The results also show that visual features are effective in alleviating the data sparsity and cold start problems on personalized tour recommendation.

Online search engine has been widely regarded as the most convenient approach for information acquisition. Indeed, the intensive information-seeking behaviors of search engine users make it possible to exploit search engine queries as effective “crowd sensors” for event monitoring. While some researchers have investigated the feasibility of using search engine queries for coarse-grained event analysis, the capability of search engine queries for real-time event detection has been largely neglected. To this end, in this article, we introduce a large-scale and systematic study on exploiting real-time search engine queries for outbreak event detection, with a focus on earthquake rapid reporting. In particular, we propose a realistic system of real-time earthquake detection through monitoring millions of queries related to earthquakes from a dominant online search engine in China. Specifically, we first investigate a large set of queries for selecting the representative queries that are highly correlated with the outbreak of earthquakes. Then, based on the real-time streams of selected queries, we design a novel machine learning–enhanced two-stage burst detection approach for detecting earthquake events. Meanwhile, the location of an earthquake epicenter can be accurately estimated based on the spatial-temporal distribution of search engine queries. Finally, through the extensive comparison with earthquake catalogs from China Earthquake Networks Center, 2015, the detection precision of our system can achieve 87.9%, and the accuracy of location estimation (province level) is 95.7%. In particular, 50% of successfully detected results can be found within 62 s after earthquake, and 50% of successful locations can be found within 25.5 km of seismic epicenter. Our system also found more than 23.3% extra earthquakes that were felt by people but not publicly released, 12.1% earthquake-like special outbreaks, and meanwhile, revealed many interesting findings, such as the typical query patterns of earthquake rumor and regular memorial events. Based on these results, our system can timely feed back information to the search engine users according to various cases and accelerate the information release of felt earthquakes.

Origin-destination (OD) matrices are used widely in transportation and logistics to record the travel cost (e.g., travel speed or greenhouse gas emission) between pairs of OD regions during different intervals within a day. We model a travel cost as a distribution because when traveling between a pair of OD regions, different vehicles may travel at different speeds even during the same interval, e.g., due to different driving styles or different waiting times at intersections. This yields stochastic OD matrices. We consider an increasingly pertinent setting where a set of vehicle trips is used for instantiating OD matrices. Since the trips may not cover all OD pairs for each interval, the resulting OD matrices are likely to be sparse. We then address the problem of forecasting complete, near future OD matrices from sparse, historical OD matrices. To solve this problem, we propose a generic learning framework that (i) employs matrix factorization and graph convolutional neural networks to contend with the data sparseness while capturing spatial correlations and that (ii) captures spatio-temporal dynamics via recurrent neural networks extended with graph convolutions. Empirical studies using two taxi trajectory data sets offer detailed insight into the properties of the framework and indicate that it is effective.

Revenue prediction is an essential component in security analysis since the revenue of a company has a great impact on the performance of its stock. For investment, one of the most valuable pieces of information is the company’s unexpected revenue, which is the difference between the officially reported revenue and the consensus estimate for revenue predicted by analysts. Since it is the unexpected revenue that indicates something exceeding or under analysts’ expectation, it is an indispensable factor that influences the performance of a stock. Besides conventional trading data from stock market and companies’ financial reports, recent years have witnessed an extensive application of alternative data for gaining an information edge in stock investment.In this paper, we study the challenging problem of better predicting unexpected revenue of a company via machine learning with alternative data. To the best of our knowledge, this is the first work studying this problem in literature. However, it is nontrivial to quantitatively model the relations between the unexpected revenue and the information provided by alternative data with a machine learning approach. Thus we proposed an adaptive master-slave regularized model, called AMS for short, to effectively leverage alternative data for unexpected revenue prediction. AMS first trains a master model upon a company graph, which captures the relations among companies, using a graph neural network (GNN). Then for a target company, the master model generates an adaptive slave-model, which is specially optimized for this target company. Finally, we use this slave-model to predict the unexpected revenue of the target company. Besides its excellent prediction performance, another critical advantage of our AMS model lies in its superior interpretability, which is crucial for portfolio managers to understand the predicted results. With extensive experiments using two real-world alternative datasets, we have demonstrated the effectiveness of our model against a set of competitors.

Labeling data comprehensively and efficiently is a widely needed but challenging task. With limited computing resources, given a data stream and a collection of deep-learning models, we propose to adaptively select and schedule a subset of these models to execute, aiming to maximize the value of the model output. Achieving this goal is nontrivial since a model's output on any data item is content-dependent and hard to predict. In this paper, we present an Adaptive Model Scheduling framework, consisting of 1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and 2) two heuristic algorithms to adaptively schedule models under deadline or deadline-memory constraints. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on 30 popular image labeling models to demonstrate the effectiveness of our design.

Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional turbulence forecasting approaches heavily rely on painstakingly customized turbulence indexes, which are less effective in dynamic and complex weather conditions. The recent availability of high-resolution weather data and turbulence records allows more accurate forecasting of the turbulence in a data-driven way. However, it is a non-trivial task for developing a machine learning based turbulence forecasting system due to two challenges: (1) Complex spatio-temporal correlations, turbulence is caused by air movement with complex spatio-temporal patterns, (2) Label scarcity, very limited turbulence labels can be obtained. To this end, in this paper, we develop a unified semi-supervised framework, T2-Net, to address the above challenges. Specifically, we first build an encoder-decoder paradigm based on the convolutional LSTM to model the spatio-temporal correlations. Then, to tackle the label scarcity problem, we propose a novel Dual Label Guessing method to take advantage of massive unlabeled turbulence data. It integrates complementary signals from the main Turbulence Forecasting task and the auxiliary Turbulence Detection task to generate pseudo-labels, which are dynamically utilized as additional training data. Finally, extensive experimental results on a real-world turbulence dataset validate the superiority of our method on turbulence forecasting.

The development of effective cooperative and competitive strategies has been recognized as the key to the success of many companies in a globalized world. Therefore, many efforts have been made on the analysis of cooperation and competition among companies. However, existing studies either rely on labor intensive empirical analysis with specific cases or do not consider the heterogeneous company information when quantitatively measuring company relationships in a company network. More importantly, it is not clear how to generate a unified representation for cooperative and competitive strategies in a data driven way. To this end, in this paper, we provide a large-scale data driven analysis on the cooperative and competitive relationships among companies in a Sign-oriented Preference Network (SOPN). Specifically, we first exploit a Relational Graph Convolutional Network (RGCN) for generating a deep representation of the heterogeneous company features and a company relation network. Then, based on the representation, we generate two sets of preference vectors for each company by utilizing the attention mechanism to model the importance of different relations, representing their cooperative and competitive strategies respectively. Also, we design a sign constraint to model the dependency between cooperation and competition relations. Finally, we conduct extensive experiments on a real-world dataset, and verify the effectiveness of our approach. Moreover, we provide a case study to show some interesting patterns and their potential business value.

The competitive relationship of Points of Interest (POIs) refers to the degree of competition between two POIs for business opportunities from third parties in an urban area. Existing studies for competitive analysis usually focus on mining competitive relationships of entities, such as companies or products, from textual data. However, there are few studies which have a focus on competitive analysis for POIs. Indeed, the growing availability of user behavior data about POIs, such as POI reviews and human mobility data, enables a new paradigm for understanding the competitive relationships among POIs. To this end, in this paper, we study how to predict the POI competitive relationship. Along this line, a very first challenge is how to integrate heterogeneous user behavior data with the spatial features of POIs. As a solution, we first build a heterogeneous POI information network (HPIN) from POI reviews and map search data. Then, we develop a graph neural network-based deep learning framework, named DeepR, for POI competitive relationship prediction based on HPIN. Specifically, DeepR contains two components: a spatial adaptive graph neural network (SA-GNN) and a POI pairwise knowledge extraction learning (PKE) model. The SA-GNN is a novel GNN architecture with incorporating POI's spatial information and location distribution by a specially designed spatial oriented aggregation layer and spatial-dependency attentive propagation mechanism. In addition, PKE is devised to distill the POI pairwise knowledge in HPIN being useful for relationship prediction into condensate vectors with relational graph convolution and cross attention. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of our method.

Public transportation plays a critical role in people's daily life. It has been proven that public transportation is more environmentally sustainable, efficient, and economical than any other forms of travel. However, due to the increasing expansion of transportation networks and more complex travel situations, people are having difficulties in efficiently finding the most preferred route from one place to another through public transportation systems. To this end, in this paper, we present Polestar, a data-driven engine for intelligent and efficient public transportation routing.Specifically, we first propose a novel Public Transportation Graph (PTG) to model public transportation system in terms of various travel costs, such as time or distance. Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation. After that, we propose a two-pass route candidate ranking module to capture user preferences under dynamic travel situations. Finally, experiments on two real-world data sets demonstrate the advantages of Polestar in terms of both efficiency and effectivenes Indeed, in early 2019, Polestar has been deployed on Baidu Maps, one of the world's largest map services. To date, Polestar is servicing over 330 cities, answers over a hundred millions of queries each day, and achieves substantial improvement of user click ratio.

Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set. In practice, temporal sets prediction is much more complex than predictive modelling of temporal events and time series, and is still an open problem. Many possible existing methods, if adapted for the problem of temporal sets prediction, usually follow a two-step strategy by first projecting temporal sets into latent representations and then learning a predictive model with the latent representations. The two-step approach often leads to information loss and unsatisfactory prediction performance. In this paper, we propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective of our approach is to learn element relationship by constructing set-level co-occurrence graph and then perform graph convolutions on the dynamic relationship graphs. Moreover, we design an attention-based module to adaptively learn the temporal dependency of elements and sets. Finally, we provide a gated updating mechanism to find the hidden shared patterns in different sequences and fuse both static and dynamic information to improve the prediction performance. Experiments on real-world data sets demonstrate that our approach can achieve competitive performances even with a portion of the training data and can outperform existing methods with a significant margin.

Given a set of locations in a city, on which ones should we place ads on so as to reach as many people as possible within a limited budget? Past research has addressed this question under the assumption that dense trajectory data are available to determine the reach of each ad. However, the data that are available in most industrial settings do not consist of dense, long-range trajectories; instead, they consist of statistics on people's short-range point-to-point movements. In this paper, we address the natural problem that arises such data: given a distribution of population and point-to-point movement statistics over a network, find a set of locations within a budget that achieves maximum expected reach. We call this problem geodemographic influence maximization (GIM). We show that the problem is NP-hard, but its objective function is monotone and submodular, thus admits a greedy algorithm with a 1 over 2 (1-1 over e) approximation ratio. Still, this algorithm is inapplicable on large-scale data for high-frequency digital signage ads. We develop an efficient deterministic algorithm, Lazy-Sower, exploiting a novel, tight double-bounding scheme of marginal influence gain as well as the locality proprieties of the problem; a learning-based variant, NN-Sower, utilizes randomization and deep learning to further improve efficiency, with a slight loss of quality. Our exhaustive experimental study on two real-world urban datasets demonstrates the efficacy and efficiency of our solutions compared to baselines.

A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is only a very limited amount of design solutions that can be tested. It is time-consuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.

Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning; (ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods.

Many sequential behaviors such as purchasing items from time to time, selecting courses in different terms, collecting event logs periodically could be formalized as sequential sets of actions or elements, namely temporal sets. Predicting the subsequent set according to historical sequence of sets could help us make better producing, scheduling, or operating decisions. However, most of the existing methods were designed for predicting time series or temporal events, which could not be directly used for temporal sets prediction due to the difficulties of multi-level representations of items and sets, complex temporal dependencies of sets, and evolving dynamics of sequential behaviors. To address these issues, this paper provides a novel sets prediction method, called DSNTSP (Dual Sequential Network for Temporal Sets Prediction). Our model first learns both item-level representations and set-level representations of set sequences separately based on a transformer framework. Then, a co-transformer module is proposed to capture the multiple temporal dependencies of items and sets. Last, a gated neural module is designed to predict the subsequent set by fusing all the multi-level correlations and multiple temporal dependencies of items and sets. The experimental results on real-world data sets show that our methods lead to significant and consistent improvements as compared to other methods.

In location-based services, such as navigation and ride-hailing, it is an essential function to match a query with Point-of-Interests (POIs) for efficient destination retrieval. Indeed, due to the space limit and real-time requirement, such services usually require intermediate POI matching results when only partial search keywords are typed. While there are numerous retrieval models for general textual semantic matching, few attempts have been made for query-POI matching by considering the integration of rich spatio-temporal factors and dynamic user preferences. To this end, in this paper, we develop a spatio-temporal dual graph attention network ~(STDGAT), which can jointly model dynamic situational context and users' sequential behaviors for intelligent query-POI matching. Specifically, we first utilize a semantic representation block to model semantic correlations among incomplete texts as well as various spatio-temporal factors captured by location and time. Next, we propose a novel dual graph attention network to capture two types of query-POI relevance, where one models global query-POI interaction and another one models time-evolving user preferences on destination POIs. Moreover, we also incorporate spatio-temporal factors into the dual graph attention network so that the query-POI relevance can be generalized to the sophisticated situational context. After that, a pairwise fusion strategy is introduced to extract the salient global feature representatives for both queries and POIs. Finally, several cold-start strategies and training methods are proposed to improve the matching effectiveness and training efficiency. Extensive experiments on two real-world datasets demonstrate the performances of our approach compared with state-of-the-art baselines. The results show that our model achieves significant improvement in terms of matching accuracy even with only partial query keywords are given.

The next-item recommendation has attracted great research interests with both static and dynamic users' preferences considered. Existing approaches typically utilize user-item binary relations, and assume a flat preference distribution over items for each user. However, this assumption neglects the hierarchical discrimination between user intentions and user preferences, causing the methods have limited capacity to depict intention-specific preference. In fact, a consumer's purchasing behavior involves a natural sequential process, i.e., he/she first has an intention to buy one type of items, followed by choosing a specific item according to his/her preference under this intention. To this end, we propose a novel key-array memory network (KA-MemNN), which takes both user intentions and preferences into account for next-item recommendation. Specifically, the user behavioral intention tendency is determined through key addressing. Further, each array outputs an intention-specific preference representation of a user. Then, the degree of user's behavioral intention tendency and intention-specific preference representation are combined to form a hierarchical representation of a user. This representation is further utilized to replace the static profile of users in traditional matrix factorization for the purposes of reasoning. The experimental results on real-world data demonstrate the advantages of our approach over state-of-the-art methods.

Visual aesthetics of products plays an important role in the decision process when purchasing appearance-first products, e.g., clothes. Indeed, user’s aesthetic preference, which serves as a personality trait and a basic requirement, is domain independent and could be used as a bridge between domains for knowledge transfer. However, existing work has rarely considered the aesthetic information in product images for cross-domain recommendation. To this end, in this paper, we propose a new deep Aesthetic Cross-Domain Networks (ACDN), in which parameters characterizing personal aesthetic preferences are shared across networks to transfer knowledge between domains. Specifically, we first leverage an aesthetic network to extract aesthetic features. Then, we integrate these features into a cross-domain network to transfer users’ domain independent aesthetic preferences. Moreover, network cross-connections are introduced to enable dual knowledge transfer across domains. Finally, the experimental results on real-world datasets show that our proposed model ACDN outperforms benchmark methods in terms of recommendation accuracy.

As a major component of strategic talent management, learning and development (L&D) aims at improving the individual and organization performances through planning tailored training for employees to increase and improve their skills and knowledge. While many companies have developed the learning management systems (LMSs) for facilitating the online training of employees, a long-standing important issue is how to achieve personalized training recommendations with the consideration of their needs for future career development. To this end, in this paper, we propose an explainable personalized online course recommender system for enhancing employee training and development. A unique perspective of our system is to jointly model both the employees’ current competencies and their career development preferences in an explainable way. Specifically, the recommender system is based on a novel end-to-end hierarchical framework, namely Demand-aware Collaborative Bayesian Variational Network (DCBVN). In DCBVN, we first extract the latent interpretable representations of the employees’ competencies from their skill profiles with autoencoding variational inference based topic modeling. Then, we develop an effective demand recognition mechanism for learning the personal demands of career development for employees. In particular, all the above processes are integrated into a unified Bayesian inference view for obtaining both accurate and explainable recommendations. Finally, extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of DCBVN, as well as its robustness on sparse and cold-start scenarios.

Recent years have witnessed the growing interests in investigating the competition among companies. Existing studies for company competitive analysis generally rely on subjective survey data and inferential analysis. Instead, in this paper, we aim to develop a new paradigm for studying the competition among companies through the analysis of talent flows. The rationale behind this is that the competition among companies usually leads to talent movement. Along this line, we first build a Talent Flow Network based on the large-scale job transition records of talents, and formulate the concept of “competitiveness” for companies with consideration of their bi-directional talent flows in the network. Then, we propose a Talent Flow Embedding (TFE) model to learn the bi-directional talent attractions of each company, which can be leveraged for measuring the pairwise competitive relationships between companies. Specifically, we employ the random-walk based model in original and transpose networks respectively to learn representations of companies by preserving their competitiveness. Furthermore, we design a multi-task strategy to refine the learning results from a fine-grained perspective, which can jointly embed multiple talent flow networks by assuming the features of company keep stable but take different roles in networks of different job positions. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness of our TFE model in terms of company competitive analysis and reveal some interesting rules of competition based on the derived insights on talent flows.

Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.

It is always deemed crucial to identify the key factors that could have significant impact on the stock market trend. Recently, an interesting phenomenon has emerged that some of President Trump’s posts in Twitter can surge into a dominant role on the stock market for a certain time period, although studies along this line are still in their infancy. Therefore, in this article, we study whether and how this new-rising information can help boost the performance of stock market prediction. Specifically, we have found that the echoing reinforced effect of financial news with Trump’s market-related tweets can influence the market movement—that is, some of Trump’s tweets directly impact the stock market in a short time, and the impact can be further intensified when it echoes with other financial news reports. Along this line, we propose a deep information echoing model to predict the hourly stock market trend, such as the rise and fall of the Dow Jones Industrial Average. In particular, to model the discovered echoing reinforced impact, we design a novel information echoing module with a gating mechanism in a sequential deep learning framework to capture the fused knowledge from both Trump’s tweets and financial news. Extensive experiments have been conducted on the real-world U.S. stock market data to validate the effectiveness of our model and its interpretability in understanding the usability of Trump’s posts. Our proposed deep echoing model outperforms other baselines by achieving the best accuracy of 60.42% and obtains remarkable accumulated profits in a trading simulation, which confirms our assumption that Trump’s tweets contain indicative information for short-term market trends. Furthermore, we find that Trump’s tweets about trade and political events are more likely to be associated with short-term market movement, and it seems interesting that the impact would not degrade as time passes.

Human mobility annotation aims to assign mobility records the corresponding visiting Point-of-Interests (POIs). It is one of the most fundamental problems for understanding human mobile behaviors. In literature, many efforts have been devoted to annotating mobility records in a pointwise or trajectory-wise manner. However, the user preference factor is not fully explored and, worse still, the mobile peer influence factor has never been integrated. To this end, in this article, we propose a novel framework, named JEPPI, to jointly exploit user preference and mobile peer influence to tackle the problem. In our JEPPI, we first unify the two distinct factors in a behavior-driven user-POI graph. This graph enables us to model user preference with user-POI visiting relationships, and model two types of mobile peer influence with co-location and co-visiting peer relationships, respectively. Moreover, we devise an equivalence-emphasizing metric to reduce redundancy in the second-order co-visiting peer influence. In addition, a mutual augmentation learning approach is proposed to preserve the latent structures of various factors exploited. Notably, our learning approach preserves all factors in a shared representation space such that user preference is learned with mobile peer influence being considered at the same time, and vice versa. In this way, the different factors are mutually augmented and semantically integrated to enhance human mobility annotation. Finally, using two large-scale real-world datasets, we conduct extensive experiments to demonstrate the superiority of our approach compared with the state-of-the-art annotation methods.

A Point-of-Interest (POI) refers to a specific location that people may find useful or interesting. In modern cities, a large number of POIs emerge, grow, stabilize for a period, then finally disappear. The stages (e.g., emerge and grow) in this process are called lifetime statuses of a POI. While a large body of research has been devoted to identifying and recommending POIs, there are few studies on inferring the lifetime status of POIs. Indeed, the predictive analytics of POI lifetime status can be valuable for various tasks, such as urban planning, business site selection, and real estate appraisal. In this article, we propose a multitask learning approach, named inferring POI lifetime status, to inferring the POI lifetime status with multifaceted data sources. Specifically, we first define three types of POI lifetime status, i.e., booming, decaying, and stable. Then, we formulate a serial classification problem to predict the sequential/successive lifetime statuses of POIs over time. Leveraging geographical data and human mobility data, we examine and integrate three aspects of features related to the prosperity of POIs, i.e., region popularity, region demands, and peer competitiveness. Next, as the booming/decaying POIs are relatively rare in our data, we perform stable class decomposition to alleviate the imbalance between stable POIs and booming/decaying POIs. Finally, we develop a POI lifetime status classifier by exploiting the multitask learning framework as well as the multiclass kernel-based vector machines. We perform extensive experiments using large-scale and real-world datasets of New York City. The experimental results validate the effectiveness of our approach to automatically inferring POI lifetime status.

Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity , which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.

Destination prediction is an essential task in various mobile applications and up to now many methods have been proposed. However, existing methods usually suffer from the problems of heavy computational burden, data sparsity, and low coverage. Therefore, a novel approach named DestPD is proposed to tackle the aforementioned problems. Differing from an earlier approach that only considers the starting and current location of a partial trip, DestPD first determines the most likely future location and then predicts the destination. It comprises two phases, the offline training and the online prediction. During the offline training, transition probabilities between two locations are obtained via Markov transition matrix multiplication. In order to improve the efficiency of matrix multiplication, we propose two data constructs, Efficient Transition Probability (ETP) and Transition Probabilities with Detours (TPD). They are capable of pinpointing the minimum amount of needed computation. During the online prediction, we design Obligatory Update Point (OUP) and Transition Affected Area (TAA) to accelerate the frequent update of ETP and TPD for recomputing the transition probabilities. Moreover, a new future trajectory prediction approach is devised. It captures the most recent movement based on a query trajectory. It consists of two components: similarity finding through Best Path Notation (BPN) and best node selection. Our novel BPN similarity finding scheme keeps track of the nodes that induces inefficiency and then finds similarity fast based on these nodes. It is particularly suitable for trajectories with overlapping segments. Finally, the destination is predicted by combining transition probabilities and the most probable future location through Bayesian reasoning. The DestPD method is proved to achieve one order of cut in both time and space complexity. Furthermore, the experimental results on real-world and synthetic datasets have shown that DestPD consistently surpasses the state-of-the-art methods in terms of both efficiency (approximately over 100 times faster) and accuracy.

Employer Brand Evaluation (EBE) is to understand an employer’s unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers’ financial information, including financial reports and filings submitted to the Securities and Exchange Commission (SEC), which may not be readily available for private companies. Fortunately, online recruitment services provide a variety of employers’ information from their employees’ online ratings and comments, which enables EBE from an employee’s perspective. To this end, in this article, we propose a method named Company Profiling–based Collaborative Topic Regression (CPCTR) to collaboratively model both textual (i.e., reviews) and numerical information (i.e., salaries and ratings) for learning latent structural patterns of employer brands. With identified patterns, we can effectively conduct both qualitative opinion analysis and quantitative salary benchmarking. Moreover, a Gaussian processes--based extension, GPCTR, is proposed to capture the complex correlation among heterogeneous information. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and generalizability of our methods in real-life applications. The results clearly show that our methods outperform state-of-the-art baselines and enable a comprehensive understanding of EBE.

The widespread use of online recruitment services has led to an information explosion in the job market. As a result, recruiters have to seek intelligent ways for Person-Job Fit, which is the bridge for adapting the right candidates to the right positions. Existing studies on Person-Job Fit usually focus on measuring the matching degree between talent qualification and job requirements mainly based on the manual inspection of human resource experts, which could be easily misguided by the subjective, incomplete, and inefficient nature of human judgment. To that end, in this article, we propose a novel end-to-end T opic-based A bility-aware P erson- J ob F it N eural N etwork (TAPJFNN) framework, which has a goal of reducing the dependence on manual labor and can provide better interpretability about the fitting results. The key idea is to exploit the rich information available in abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, two hierarchical topic-based ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measure the different contribution of each job experience to a specific ability requirement. In addition, we design a refinement strategy for Person-Job Fit prediction based on historical recruitment records. Furthermore, we introduce how to exploit our TAPJFNN framework for enabling two specific applications in talent recruitment: talent sourcing and job recommendation. Particularly, in the application of job recommendation, a novel training mechanism is designed for addressing the challenge of biased negative labels. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness and interpretability of the TAPJFNN and its variants compared with several baselines.

Demand forecasting factory production is of particular importance for retailers of perishable goods, as they are produced daily with a fixed production lead time. Over- or underestimating demand can result in loss of profits due to stock-outs or overstock. However, demand forecasting and production planning for perishable goods represent a significant challenge due to factors such as high volatility, significant variation, the dynamics of store-level product demand, and the need for L day ahead forecasting that allows enough time for production planning. By collaborating with a leading perishable product retailer, we have analyzed (1) detailed internal supply chain data, including sales transaction records and day-end inventories, along with (2) environmental factors, including temperature, weather conditions and wind speed. With the aim of minimizing loss of profit caused by inaccurate forecasting, we propose the following three-stage hierarchical demand forecasting model that leverages the combined data for perishable goods production planning: 1. Identification of store-level demand patterns, 2. store clustering for aggregated production, and 3. a recurrent dynamic network based on a nonlinear autoregressive network with exogenous inputs (NARX) for L-day ahead demand forecasting. Finally, we validate the proposed approach by comparing the loss of profits using this model with other baselines along with the industry standard model used in the perishable goods industry. Our proposed model successfully reduces lost profits to 3.30% of total sales, representing a reduction of 1.71% when compared with the industry standard production system.

Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. JTB could provide precise guidance and considerable convenience for both talent recruitment and job seekers for position and salary calibration/prediction. Traditional JTB approaches mainly rely on manual market surveys, which is expensive and labor intensive. Recently, the rapid development of Online Professional graph has accumulated a large number of talent career records, which provides a promising trend for data-driven solutions. However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for a same position (\eg,Programmer, Software Development Engineer, SDE, Implementation Engineer ), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness for modeling job transition patterns. To overcome these challenges, we aggregate all the records to construct a large-scale Job Title Benchmarking Graph (Job-Graph), where nodes denote job titles affiliated with specific companies and links denote the correlations between jobs. We reformulate the JTB as the task of link prediction over the Job-Graph that matched job titles should have links. Along this line, we propose a collective multi-view representation learning method (Job2Vec) by examining the Job-Graph jointly in (1) graph topology view (the structure of relationships among job titles), (2) semantic view (semantic meaning of job descriptions), (3) job transition balance view (the numbers of bidirectional transitions between two similar-level jobs are close), and (4) job transition duration view (the shorter the average duration of transitions is, the more similar the job titles are). We fuse the multi-view representations in the encode-decode paradigm to obtain an unified optimal representations for the task of link prediction. Finally, we conduct extensive experiments to validate the effectiveness of our proposed method.

Since the financial crisis in late 2008-2009, several global regulatory authorities have mandated stress-testing exercises to evaluate the potential capital shortfalls & systemic impacts that large banks may face during adverse economic conditions. Thus, having the ability to analyze economic conditions & banking performance profiles together to determine relationships among their respective features may provide insights for stress testing tasks. In this paper, we propose an Integrated Multimodal Bank Stress Test Prediction (IMBSTP) model framework consisting of a two-stages; (1) economic conditions estimator to approximate joint representation among the exogenous factors using generative models, (2) bank capital & loss forecaster to project stress test measures based on dimensional & temporal features selected from the exogenous economic conditions & banking performance profiles using a dual-attention recurrent neural network. Extensive experimentation is performed on historical economic conditions & consolidated financial statements of U.S. bank holdings companies to show the effectiveness of our approach when compared to state-of-the-art baseline methods.

How to identify high-potential talent (HIPO) earlier in their career always has strategic importance for human resource management. While tremendous efforts have been made in this direction, most existing approaches are still based on the subjective selection of human resource experts. This could lead to unintentional bias and inconsistencies. To this end, in this paper, we propose a neural network based dynamic social profiling approach for quantitatively identifying HIPOs from the newly-enrolled employees by modeling the dynamics of their behaviors in organizational social networks. A basic assumption is that HIPOs usually perform more actively and have higher competencies than their peers to accumulate their social capitals during their daily work practice. Along this line, we first propose to model the social profiles of employees with both Graph Convolutional Network (GCN) and social centrality analysis in a comprehensive way. Then, an adaptive Long Short Term Memory (LSTM) network with global attention mechanism is designed to capture the profile dynamics of employees in the organizational social networks during their early career. Finally, extensive experiments on real-world data clearly validate the effectiveness of our approach as well as the interpretability of our results.

Aftershocks refer to the smaller earthquakes that occur following large earthquakes, in the same area of the main shock. The task of aftershocks detection, as a crucial and challenging issue in disaster monitoring, has attracted wide research attention in relevant fields. Compared with the traditional detection methods like STA/LTA algorithms or heuristic matching, neural network techniques are regarded as an advanced choice with better pattern recognition ability. However, current neural network-based solutions mainly formulate the seismic wave as ordinary time series, where existing techniques are directly deployed without adaption, and thus fail to obtain competitive performance on the intensive and highly-noise waveforms of aftershocks. To that end, in this paper, we propose a novel framework named Multi-Scale Description based Neural Network (MSDNN) for enhancing aftershock detection. Specifically, MSDNN contains a delicately-designed network structure for capturing both short-term scale and long-term scale seismic features. Therefore, the unique characteristics of seismic waveforms can be fully-exploited for aftershock detection. Furthermore, a multi-task learning strategy is introduced to model the seismic waveforms of multiple monitoring stations simultaneously, which can not only refine the detection performance but also provide additionally quantitative clues for discovering homologous earthquakes. Finally, comprehensive experiments on the data set from aftershocks of the Wenchuan M8.0 Earthquake have clearly validated the effectiveness of our framework compared with several state-of-the-art baselines.

Transportation recommendation is one important map service in navigation applications. Previous transportation recommendation solutions fail to deliver satisfactory user experience because their recommendations only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and largely overlook situational context. In this work, we propose Hydra, a recommendation system that offers multi-modal transportation planning and is adaptive to various situational context (e.g., nearby point-of-interest (POI) distribution and weather). We leverage the availability of existing routing engines and big urban data, and design a novel two-level framework that integrates uni-modal and multi-modal (e.g., taxi-bus, bus-cycle) routes as well as heterogeneous urban data for intelligent multi-modal transportation recommendation. In addition to urban context features constructed from multi-source urban data, we learn the latent representations of users, origin-destination (OD) pairs and transportation modes based on user implicit feedbacks, which captures the collaborative transportation mode preferences of users and OD pairs. A gradient boosting tree based model is then introduced to recommend the proper route among various uni-modal and multi-modal transportation routes. We also optimize the framework to support real-time, large-scale route query and recommendation. We deploy Hydra on Baidu Maps, one of the world's largest map services. Real-world urban-scale experiments demonstrate the effectiveness and efficiency of our proposed system. Since its deployment in August 2018, Hydra has answered over a hundred million route recommendation queries made by over ten million distinct users with 82.8% relative improvement of user click ratio.

The understanding of job mobility can benefit talent management operations in a number of ways, such as talent recruitment, talent development, and talent retention. While there is extensive literature showing the predictability of the organization-level job mobility patterns (e.g., in terms of the employee turnover rate), there are no effective solutions for supporting the understanding of job mobility at an individual level. To this end, in this paper, we propose a hierarchical career-path-aware neural network for learning individual-level job mobility. Specifically, we aim at answering two questions related to individuals in their career paths: 1) who will be the next employer? 2) how long will the individual work in the new position? Specifically, our model exploits a hierarchical neural network structure with embedded attention mechanism for characterizing the internal and external job mobility. Also, it takes personal profile information into consideration in the learning process. Finally, the extensive results on real-world data show that the proposed model can lead to significant improvements in prediction accuracy for the two aforementioned prediction problems. Moreover, we show that the above two questions are well addressed by our model with a certain level of interpretability. For the case studies, we provide data-driven evidence showing interesting patterns associated with various factors (e.g., job duration, firm type, etc.) in the job mobility prediction process.

In talent recruitment, the job interview aims at selecting the right candidates for the right jobs through assessing their skills and experiences in relation to the job positions. While tremendous efforts have been made in improving job interviews, a long-standing challenge is how to design appropriate interview questions for comprehensively assessing the competencies that may be deemed relevant and representative for person-job fit. To this end, in this research, we focus on the development of a personalized question recommender system, namely DuerQuiz, for enhancing the job interview assessment. DuerQuiz is a fully deployed system, in which a knowledge graph of job skills, Skill-Graph, has been built for comprehensively modeling the relevant competencies that should be assessed in the job interview. Specifically, we first develop a novel skill entity extraction approach based on a bidirectional Long Short-Term Memory (LSTM) with a Conditional Random Field (CRF) layer (LSTM-CRF) neural network enhanced with adapted gate mechanism. In particular, to improve the reliability of extracted skill entities, we design a label propagation method based on more than 10 billion click-through data from the large-scale Baidu query logs. Furthermore, we discover the hypernym-hyponym relations between skill entities and construct the Skill-Graph by leveraging the classifier trained with extensive contextual features. Finally, we design a personalized question recommendation algorithm based on the Skill-Graph for improving the efficiency and effectiveness of job interview assessment. Extensive experiments on real-world recruitment data clearly validate the effectiveness of DuerQuiz, which had been deployed for generating written exercises in the 2018 Baidu campus recruitment event and received remarkable performances in terms of efficiency and effectiveness for selecting outstanding talents compared with a traditional non-personalized human-only assessment approach.

Person-Organization fit (P-O fit) refers to the compatibility between employees and their organizations. The study of P-O fit is important for enhancing proactive talent management. While considerable efforts have been made in this direction, it still lacks a quantitative and holistic way for measuring P-O fit and its impact on talent management. To this end, in this paper, we propose a novel data-driven neural network approach for dynamically modeling the compatibility in P-O fit and its meaningful relationships with two critical issues in talent management, namely talent turnover and job performance. Specifically, inspired by the practical management scenarios, we first creatively design an Organizational Structure-aware Convolutional Neural Network (OSCN) for hierarchically extracting organization-aware compatibility features for measuring P-O fit. Then, to capture the dynamic nature of P-O fit and its consequent impact, we further exploit an adapted Recurrent Neural Network with attention mechanism to model the temporal information of P-O fit. Finally, we compare our approach with a number of state-of-the-art baseline methods on real-world talent data. Experimental results clearly demonstrate the effectiveness in terms of turnover prediction and job performance prediction. Moreover, we also show some interesting indicators of talent management through the visualization of network layers.

Mobile user profiles are a summary of characteristics of user-specific mobile activities. Mobile user profiling is to extract a user's interest and behavioral patterns from mobile behavioral data. While some efforts have been made for mobile user profiling, existing methods can be improved via representation learning with awareness of substructures in users' behavioral graphs. Specifically, in this paper, we study the problem of mobile users profiling with POI check-in data. To this end, we first construct a graph, where a vertex is a POI category and an edge is the transition frequency of a user between two POI categories, to represent each user. We then formulate mobile user profiling as a task of representation learning from user behavioral graphs. We later develop a deep adversarial substructured learning framework for the task. This framework has two mutually-enhanced components. The first component is to preserve the structure of the entire graph, which is formulated as an encoding-decoding paradigm. In particular, the structure of the entire graph is preserved by minimizing reconstruction loss between an original graph and a reconstructed graph. The second component is to preserve the structure of subgraphs, which is formulated as a substructure detector based adversarial training paradigm. In particular, this paradigm includes a substructure detector and an adversarial trainer. Instead of using non-differentiable substructure detection algorithms, we pre-train a differentiable convolutional neural network as the detector to approximate these detection algorithms. The adversarial trainer is to match the detected substructure of the reconstructed graph to the detected substructure of the original graph. Also, we provide an effective solution for the optimization problems. Moreover, we exploit the learned representations of users for the next activity type prediction. Finally, we present extensive experimental results to demonstrate the improved performances of the proposed method.

Recent years have witnessed growing interests in developing deep models for incremental learning. However, existing approaches often utilize the fixed structure and online backpropagation for deep model optimization, which is difficult to be implemented for incremental data scenarios. Indeed, for streaming data, there are two main challenges for building deep incremental models. First, there is a requirement to develop deep incremental models with Capacity Scalability. In other words, the entire training data are not available before learning the task. It is a challenge to make the deep model structure scaling with streaming data for flexible model evolution and faster convergence. Second, since the stream data distribution usually changes in nature (concept drift), there is a constraint for Capacity Sustainability. That is, how to update the model while preserving previous knowledge for overcoming the catastrophic forgetting. To this end, in this paper, we develop an incremental adaptive deep model (IADM) for dealing with the above two capacity challenges in real-world incremental data scenarios. Specifically, IADM provides an extra attention model for the hidden layers, which aims to learn deep models with adaptive depth from streaming data and enables capacity scalability. Also, we address capacity sustainability by exploiting the attention based fisher information matrix, which can prevent the forgetting in consequence. Finally, we conduct extensive experiments on real-world data and show that IADM outperforms the state-of-the-art methods with a substantial margin. Moreover, we show that IADM has better capacity scalability and sustainability in incremental learning scenarios.

Taxi and sharing bike bring great convenience to urban transportation. A lot of efforts have been made to improve the efficiency of taxi service or bike sharing system by predicting the next-period pick-up or drop-off demand. Different from the existing research, this paper is motivated by the following two facts: 1) From a micro view, an observed spatial demand at any time slot could be decomposed as a combination of many hidden spatial demand bases; 2) From a macro view, the multiple transportation demands are strongly correlated with each other, both spatially and temporally. Definitely, the above two views have great potential to revolutionize the existing taxi or bike demand prediction methods. Along this line, this paper provides a novel Co-prediction method based on Spatio-Temporal neural Network, namely, CoST-Net. In particular, a deep convolutional neural network is constructed to decompose a spatial demand into a combination of hidden spatial demand bases. The combination weight vector is used as a representation of the decomposed spatial demand. Then, a heterogeneous Long Short-Term Memory (LSTM) is proposed to integrate the states of multiple transportation demands, and also model the dynamics of them mixedly. Last, the environmental features such as humidity and temperature are incorporated with the achieved overall hidden states to predict the multiple demands simultaneously. Experiments have been conducted on real-world taxi and sharing bike demand data, results demonstrate the superiority of the proposed method over both classical and the state-of-the-art transportation demand prediction methods.

Multi-task learning is a successful machine learning framework which improves the performance of prediction models by leveraging knowledge among tasks, e.g., the relationships between different tasks. Most of existing multi-task learning methods focus on guiding learning process by predefined task relationships. In fact, these methods have not fully exploited the associated relationships during the learning process. On the one hand, replacing predefined task relationships by adaptively learned ones may result in higher prediction accuracy as it can avoid the risk of misguiding caused by improperly predefined relationships. On the other hand, apart from the task relationships, feature-task dependence and feature-feature interactions could also be employed to guide the learning process. Along this line, we propose aMultiple Relational Attention Network (MRAN) framework for multi-task learning, in which three types of relationships are considered. Correspondingly, MRAN consists of three attention-based relationship learning modules: 1) a task-task relationship learning module which captures the relationships among tasks automatically and controls the positive and negative knowledge transfer adaptively; 2) a feature-feature interaction learning module that handles the complicated interactions among features; 3) a task-feature dependence learning module, which can associate the related features with target tasks separately. To evaluate the effectiveness of the proposed MARN, experiments are conducted on two public datasets and a real-world dataset crawled from a review hosting site. Experimental results demonstrate the superiority of our method over both classical and the state-of-the-art multi-task learning methods.

Tags of a Point of Interest (POI) can facilitate location-based services from many aspects like location search and place recommendation. However, many POI tags are often incomplete or imprecise, which may lead to performance degradation of tag-dependent applications. In this paper, we study the POI tag refinement problem which aims to automatically fill in the missing tags as well as correct noisy tags for POIs. We propose a tri-adaptive collaborative learning framework to search for an optimal POI-tag score matrix. The framework integrates three components to collaboratively (i) model the similarity matching between POI and tag, (ii) recover the POI-tag pattern via matrix factorization and (iii) learn to infer the most possible tags by maximum likelihood estimation. We devise an adaptively joint training process to optimize the model and regularize each component simultaneously. And the final refinement results are the consensus of multiple views from different components. We also discuss how to utilize various data sources to construct features for tag refinement, including user profile data, query data on Baidu Maps and basic properties of POIs. Finally, we conduct extensive experiments to demonstrate the effectiveness of our framework. And we further present a case study of the deployment of our framework on Baidu Maps.

The sequential recommendation, which models sequential behavioral patterns among users for the recommendation, plays a critical role in recommender systems. However, the state-of-the-art Recurrent Neural Networks (RNN) solutions rarely consider the non-linear feature interactions and non-monotone short-term sequential patterns, which are essential for user behavior modeling in sparse sequence data. In this paper, we propose a novel Recurrent Convolutional Neural Network model (RCNN). It not only utilizes the recurrent architecture of RNN to capture complex long-term dependencies, but also leverages the convolutional operation of Convolutional Neural Network (CNN) model to extract short-term sequential patterns among recurrent hidden states. Specifically, we first generate a hidden state at each time step with the recurrent layer. Then the recent hidden states are regarded as an “image”, and RCNN searches non-linear feature interactions and non-monotone local patterns via intra-step horizontal and inter-step vertical convolutional filters, respectively. Moreover, the output of convolutional filters and the hidden state are concatenated and fed into a fully-connected layer to generate the recommendation. Finally, we evaluate the proposed model using four real-world datasets from various application scenarios. The experimental results show that our model RCNN significantly outperforms the state-of-the-art approaches on sequential recommendation.

The understanding of talent flow is critical for sharpening company talent strategy to keep competitiveness in the current fast-evolving environment. Existing studies on talent flow analysis generally rely on subjective surveys. However, without large-scale quantitative studies, there are limits to deliver fine-grained predictive business insights for better talent management. To this end, in this paper, we aim to introduce a big data-driven approach for predictive talent flow analysis. Specifically, we first construct a time-aware job transition tensor by mining the large-scale job transition records of digital resumes from online professional networks (OPNs), where each entry refers to a fine-grained talent flow rate of a specific job position between two companies. Then, we design a dynamic latent factor based Evolving Tensor Factorization (ETF) model for predicting the future talent flows. In particular, a novel evolving feature by jointly considering the influence of previous talent flows and global market is introduced for modeling the evolving nature of each company. Furthermore, to improve the predictive performance, we also integrate several representative attributes of companies as side information for regulating the model inference. Finally, we conduct extensive experiments on large-scale real-world data for evaluating the model performances. The experimental results clearly validate the effectiveness of our approach compared with state-of-the-art baselines in terms of talent flow forecast. Meanwhile, the results also reveal some interesting findings on the regularity of talent flows, e.g. Facebook becomes more and more attractive for the engineers from Google in 2016.

Massive data collected by automated fare collection (AFC) systems provide opportunities for studying both personal traveling behaviors and collective mobility patterns in urban areas. Existing studies on AFC data have primarily focused on identifying passengers' movement patterns. However, we creatively leveraged such data for identifying pickpocket suspects. Stopping pickpockets in the public transit system has been crucial for improving passenger satisfaction and public safety. Nonetheless, in practice, it is challenging to discern thieves from regular passengers. In this paper, we developed a suspect detection and surveillance system, which can identify pickpocket suspects based on their daily transit records. Specifically, we first extracted a number of useful features from each passenger's daily activities in the transit system. Then, we took a two-step approach that exploits the strengths of unsupervised outlier detection and supervised classification models to identify thieves, who typically exhibit abnormal traveling behaviors. Experimental results demonstrated the effectiveness of our method. We also developed a prototype system for potential uses by security personnel.

Human mobility data refers to records of human movements, such as cellphone traces, vehicle GPS trajectories, geo-tagged posts, and photos. While successfully mining human mobility data can benefit many applications such as city planning, transportation, urban economics, and public safety, it is very challenging to model large-scale Heterogeneous Human Mobility Data (HHMD) that are generated from different resources. In this paper, we develop a general collective learning approach to model HHMD at an individual level towards identifying and quantifying the urban forms of residential communities. Specifically, our proposed method exploits two geographic regularities among HHMD. First, we jointly capture the correlations among residential communities, urban functions, temporal effects, and user mobility patterns by analogizing communities as documents and mobility patterns as words. Also, we further combine explicit LASSO analysis and significant testing into latent representation learning as a regularization term by analogizing compatible Point-of-Interests (POIs) as the meta-data of communities. In this way, we can learn the urban forms, including a mix of functions and corresponding portfolios, of residential communities from HHDM and POIs. We further leverage these learned results to address two application problems: real estate ranking and restaurant popularity prediction. Finally, we conduct intensive evaluations with a variety of real-world data, where experimental results demonstrate the effectiveness of our proposed modeling method and its successful applications for other problems.

Talent flow analysis is a process for analyzing and modeling the flows of employees into and out of targeted organizations, regions, or industries. A clear understanding of talent flows is critical for many applications, such as human resource planning, brain drain monitoring, and future workforce forecasting. However, existing studies on talent flow analysis are either qualitative or limited by coarse level quantitative modeling. To this end, in this paper, we provide a fine-grained data-driven approach to model the dynamics and evolving nature of talent flows by leveraging the rich information available in job transition networks. Specifically, we first investigate how to enrich the sparse talent flow data by exploiting the correlations between the stock price movement and the talent flows of public companies. Then, we formalize the talent flow modeling problem as to predict the increments of the edge weights in the dynamic job transition network. In this way, the problem is transformed into a multi-step time series forecasting problem. A deep sequence prediction model is developed based on the recurrent neural network model, which consumes multiple input sources derived from dynamic job transition networks. Finally, experimental results on real-world data show that the proposed model outperforms other benchmark models in terms of prediction accuracy. The results also indicate that the proposed model can provide reasonable performance even if the historical talent flow data are not completely available.

It is commonly seen that social events are organized through online social network services (SNSs), and thus there are vested interests in studying event-oriented social gathering through SNSs. The focus of existing studies has been put on the analysis of event profiles or individual participation records. While there is significant dynamic mutual influence among target users through their social connections, the impact of dynamic mutual influence on the people's social gathering remains unknown. To that end, in this paper, we develop a discriminant framework, which allows to integrate the dynamic mutual dependence of potential event participants into the discrimination process. Specifically, we formulate the group-oriented event participation problem as a two-stage variant discriminant framework to capture the users' profiles as well as their latent social connections. The validation on real-world data sets show that our method can effectively predict the event participation with a significant margin compared with several state-of-the-art baselines. This validates the hypothesis that dynamic mutual influence could play an important role in the decision-making process of social event participation. Moreover, we propose the network pruning method to further improve the efficiency of our technical framework. Finally, we provide a case study to illustrate the application of our framework for event plan design task.

As a vital process to the success of an organization, salary benchmarking aims at identifying the right market rate for each job position. Traditional approaches for salary benchmarking heavily rely on the experiences from domain experts and limited market survey data, which have difficulties in handling the dynamic scenarios with the timely benchmarking requirement. To this end, in this paper, we propose a data-driven approach for intelligent salary benchmarking based on large-scale fine-grained online recruitment data. Specifically, we first construct a salary matrix based on the large-scale recruitment data and creatively formalize the salary benchmarking problem as a matrix completion task. Along this line, we develop a Holistic Salary Benchmarking Matrix Factorization (HSBMF) model for predicting the missing salary information in the salary matrix. Indeed, by integrating multiple confounding factors, such as company similarity, job similarity, and spatial-temporal similarity, HSBMF is able to provide a holistic and dynamic view for fine-grained salary benchmarking. Finally, extensive experiments on large-scale real-world data clearly validate the effectiveness of our approach for job salary benchmarking.

Keyphrases have been widely used in large document collections for providing a concise summary of document content. While significant efforts have been made on the task of automatic keyphrase extraction, existing methods have challenges in training a robust supervised model when there are insufficient labeled data in the resource-poor domains. To this end, in this paper, we propose a novel Topic-based Adversarial Neural Network (TANN) method, which aims at exploiting the unlabeled data in the target domain and the data in the resource-rich source domain. Specifically, we first explicitly incorporate the global topic information into the document representation using a topic correlation layer. Then, domain-invariant features are learned to allow the efficient transfer from the source domain to the target by utilizing adversarial training on the topic-based representation. Meanwhile, to balance the adversarial training and preserve the domain-private features in the target domain, we reconstruct the target data from both forward and backward directions. Finally, based on the learned features, keyphrase are extracted using a tagging method. Experiments on two realworld cross-domain scenarios demonstrate that our method can significantly improve the performance of keyphrase extraction on unlabeled or insufficiently labeled target domain.

In real-world recommendation scenarios, there are two common phenomena: 1) users only provide ratings but there is no review comment. As a result, the historical transaction data available for recommender system are usually unbalanced and sparse; 2) Users' opinions can be better grasped in their reviews than ratings. This indicates that there is always a bias between ratings and reviews. Therefore, it is important that users' ratings and reviews should be mutually reinforced to grasp the users' true opinions. To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation (NeuO). Specifically, we exploit a two-step training neural networks, which utilize both reviews and ratings to grasp users' true opinions in unbalanced data. Moreover, we propose a Sentiment Classification scoring method (SC), which employs dual attention vectors to predict the users' sentiment scores of their reviews. A combination function is designed to use the results of SC and user-item rating matrix to catch the opinion bias. Finally, a Multilayer perceptron based Matrix Factorization (MMF) method is proposed to make recommendations with the enhanced user-item matrix. Extensive experiments on real-world data demonstrate that our approach can achieve a superior performance over state-of-the-art baselines on real-world datasets.

Given its importance, crime prediction has attracted a lot of attention in the literature, and several methods have been proposed to discover different aspects of characteristics for crime prediction. In this paper, we propose a Clustered Continuous Conditional Random Field (Clustered-CCRF) model which is able to effectively exploit both spatial and temporal factors for crime prediction in an integrated way. In particular, we observe that the crime number at one specific area is not only conditioned on its own historical records but also has high correlation to crime records from similar areas. Therefore, we propose two factors: an auto-regressed temporal correlation and a feature-based inter-area spatial correlation, to measure such patterns for crime prediction. Further, we present a tree-structured clustering algorithm to discover high similar areas based on spatial characteristics to improve the performance of our proposed model. Experiments on real-world crime dataset demonstrate the superiority of our proposed model over the state-of-the-art methods.

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person-Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end-to-end A bility-aware P erson-J ob F it N eural N etwork (APJFNN) model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers' experiences based on Recurrent Neural Network (RNN). Along this line, four hierarchical ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large-scale real-world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines.

Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer social trends and language constructs over different periods of human history. However, traditional techniques such as word representation learning do not adequately capture the evolving language structure and vocabulary. In this paper, we develop a dynamic statistical model to learn time-aware word vector representation. We propose a model that simultaneously learns time-aware embeddings and solves the resulting alignment problem. This model is trained on a crawled NYTimes dataset. Additionally, we develop multiple intuitive evaluation strategies of temporal word embeddings. Our qualitative and quantitative tests indicate that our method not only reliably captures this evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.

In urban transportation systems, transfer stations refer to hubs connecting a variety of bus and subway lines and, thus, are the most important nodes in transportation networks. The pervasive availability of large-scale travel traces of passengers, collected from automated fare collection (AFC) systems, has provided unprecedented opportunities for understanding citywide transfer patterns, which can benefit smart transportation, such as smart route recommendation to avoid crowded lines, and dynamic bus scheduling to enhance transportation efficiency. To this end, in this article, we provide a systematic study of the measurement, patterns, and modeling of spatiotemporal dynamics of passenger transfers. Along this line, we develop a data-driven analytical system for modeling the transfer volumes of each transfer station. More specifically, we first identify and quantify the discriminative patterns of spatiotemporal dynamics of passenger transfers by utilizing heterogeneous sources of transfer related data for each station. Also, we develop a multi-task spatiotemporal learning model for predicting the transfer volumes of a specific station at a specific time period. Moreover, we further leverage the predictive model of passenger transfers to provide crowdedness-aware route recommendations. Finally, we conduct the extensive evaluations with a variety of real-world data. Experimental results demonstrate the effectiveness of our proposed modeling method and its applications for smart transportation.

The service usage analysis, aiming at identifying customers’ messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced framework for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage subsequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach. We find that the proposed multi-label multi-view framework can help overcome the pain of mixed-usage subsequences and can be generalized to latent activity analysis in sequential data, beyond in-App usage analytics.

Human mobility analysis is one of the most important research problems in the field of urban computing. Existing research mainly focuses on the intra-city ground travel behavior modeling, while the inter-city air travel behavior modeling has been largely ignored. Actually, the inter-city travel analysis can be of equivalent importance and complementary to the intra-city travel analysis. Understanding massive passenger-air-travel behavior delivers intelligence for airlines’ precision marketing and related socioeconomic activities, such as airport planning, emergency management, local transportation planning, and tourism-related businesses. Moreover, it provides opportunities to study the characteristics of cities and the mutual relationships between them. However, modeling and predicting air traveler behavior is challenging due to the complex factors of the market situation and individual characteristics of customers (e.g., airlines’ market share, customer membership, and travelers’ intrinsic interests on destinations). To this end, in this article, we present a systematic study on the personalized air travel prediction problem, namely where a customer will fly to and which airline carrier to fly with, by leveraging real-world anonymized Passenger Name Record (PNR) data. Specifically, we first propose a relational travel topic model, which combines the merits of latent factor model with a neighborhood-based method, to uncover the personal travel preferences of aviation customers and the latent travel topics of air routes and airline carriers simultaneously. Then we present a multi-factor travel prediction framework, which fuses complex factors of the market situation and individual characteristics of customers, to predict airline customers’ personalized travel demands. Experimental results on two real-world PNR datasets demonstrate the effectiveness of our approach on both travel topic discovery and customer travel prediction.

Business to Business (B2B) marketing aims at meeting the needs of other businesses instead of individual consumers, and thus entails management of more complex business needs than consumer marketing. The buying processes of the business customers involve series of different marketing campaigns providing multifaceted information about the products or services. While most existing studies focus on individual consumers, little has been done to guide business customers due to the dynamic and complex nature of these business buying processes. To this end, in this paper, we focus on providing a unified view of social and temporal modeling for B2B marketing campaign recommendation. Along this line, we first exploit the temporal behavior patterns in the B2B buying processes and develop a marketing campaign recommender system. Specifically, we start with constructing a temporal graph as the knowledge representation of the buying process of each business customer. Temporal graph can effectively extract and integrate the campaign order preferences of individual business customers. It is also worth noting that our system is backward compatible since the participating frequency used in conventional static recommender systems is naturally embedded in our temporal graph. The campaign recommender is then built in a low-rank graph reconstruction framework based on probabilistic graphical models. Our framework can identify the common graph patterns and predict missing edges in the temporal graphs. In addition, since business customers very often have different decision makers from the same company, we also incorporate social factors, such as community relationships of the business customers, for further improving overall performances of the missing edge prediction and recommendation. Finally, we have performed extensive empirical studies on real-world B2B marketing data sets and the results show that the proposed method can effectively improve the quality of the campaign recommendations for challenging B2B marketing tasks.

Given a large transactional database, correlation computing/association analysis aims at efficiently finding strongly correlated items. For traditional association analysis, relationships among variables are usually measured at a global level. In this study, we investigate confounding factors that can help to capture abnormal correlation behaviors at a local level. Indeed, many real-world phenomena are localized to specific markets or subpopulations. Such local relationships may not be visible or may be miscalculated when collectively analyzing the entire data. In particular, confounding effects that change the direction of correlation are a most severe problem because the global correlations alone leads to errant conclusions. To this end, we propose CONFOUND, an efficient algorithm to identify paradoxical correlation patterns (i.e., where controlling for a third item changes the direction of association for strongly correlated pairs) using effective pruning strategies. Moreover, we also provide an enhanced version of this algorithm, called CONFOUND±, which substantially speeds up the confounder search step. Finally, experimental results showed that our proposed CONFOUND and CONFOUND± algorithms can effectively identify confounders and the computational performance is orders of magnitude faster than benchmark methods.

Volatile Point-of-Interests (vPOIs) refer to those small businesses which appear and disappear quickly in cities. How to maintain and incubate small business in the urban area is a big concern for both business owners and government administrators. Therefore, the prediction task for the rise and fall of vPOIs is valuable for both shopkeepers and administrators by supporting a variety of applications in urban economics. In this paper, we propose a framework, named FRFP, to predict the prosperity of vPOIs over time. Specifically, due to the data sparsity and skewness of the individual vPOIs, we first aggregate vPOIs prosperities at focal areas w.r.t. each vPOI category. Then we develop the dynamic-continuous CRF (DC-CRF) model to integrate the association between input and output as well as the correlations between outputs from temporal, spatial and contextual perspectives. Finally, we conduct empirical experiments on real-world data from Google Maps and NYC OpenData. The evaluation results demonstrate that our proposed approach outperforms baseline algorithms with considerable margins. In addition, we explore the predictability of different explanatory variables and provide actionable insights for both shopkeepers and urban planners.

Supply chain management aims at delivering goods in the shortest time at the lowest possible price while ensuring the best possible quality and is now vital to the success of the online retail business. Executing effective warehouse site selection has been one of the key challenges in the development of a successful supply chain system. While some effective strategies for warehouse site selection have been identified by the domain experts based on their experiences, the emergence of new ways of collecting fine-grained supply chain data has enabled a new paradigm for warehouse site selection. Indeed, in this paper, we provide a data-smart approach for addressing the connected capacitated warehouse location problem (CCWL), which searches for the minimum total transportation cost of the warehouse network including supplier-warehouses shipping cost, warehouse-customer delivering cost and the cost of warehouse-warehouse inter-transportation. Specifically, we first design a sales distribution prediction model and evaluate the importance of customer logistic service utilities on online market sales demand for online retailers. Then, we propose the E&M algorithm to optimize warehouse locations continuously with much less computation cost. Moreover, the computation cost is further reduced through delivery demand based Hierarchical Clustering which reduces the problem size by grouping delivering cities with close locations. Finally, we validate the proposed method on real-world e-Commerce supply chain data and the selection effect of new warehouses is evaluated in terms of sales improvement with faster delivery and more effective inventory management.

Transit advertising provides frequent exposure to a large number of residents in different urban regions. However, traditional methods are generally manual and qualitatively based on a rough estimation, such as the number of passengers taken by a bus or functional regions covered. How to accurately put an advertisement on appropriate transportations becomes an important task for potential business value. In this paper, our goal is to recommend top-k bus routes for an ad, which can maximize advertising effectiveness, by mining multiple data sources, such as Smart Card Transaction (SCT) data, geographic data and point of interests (POIs) data. We propose a framework that captures the mobility patterns of passengers and the characteristics of bus stations so as to evaluate the influence of an ad for the passengers quantitatively. Specifically, we first extract the passenger trajectories to model passenger mobility from SCT data and then model each bus station over three characteristics (i.e., hub degree, topic distribution, district region) by geographic data and nearby POIs. According to three characteristics of bus stations, we formulate three application scenarios and design the corresponding methods to make top-k bus routes recommendation. Extensive experiments on real-world public transportation data validate the effectiveness of our proposed approach.

With the rapid development of location-based social networks, Point-of-Interest (POI) recommendation has played an important role in helping people discover attractive locations. However, existing POI recommendation methods assume a flat structure of POIs, which are better described in a hierarchical structure in reality. Furthermore, we discover that both users' content and spatial preferences exhibit hierarchical structures. To this end, in this paper, we propose a hierarchical geographical matrix factorization model (HGMF) to utilize the hierarchical structures of both users and POIs for POI recommendation. Specifically, we first describe the POI influence degrees over regions with two-dimensional normal distribution, and learn the influence areas of different layers of POIs as the input of HGMF. Then, we perform matrix factorization on user content preference matrix, user spatial preference matrix, and POIs characteristic matrix jointly with the modeling of implicit hierarchical structures. Moreover, a two-step optimization method is proposed to learn the implicit hierarchical structure and find the solution of HGMF efficiently. Finally, we evaluate HGMF on two large-scale real-world location-based social networks datasets. Our experimental results demonstrate that it outperforms the state-of-the-art methods in terms of precision and recall.

Outlier detection is the identification of points in a dataset that do not conform to the norm. Outlier detection is highly sensitive to the choice of the detection algorithm and the feature subspace used by the algorithm. Extracting domain-relevant insights from outliers needs systematic exploration of these choices since diverse outlier sets could lead to complementary insights. This challenge is especially acute in an interactive setting, where the choices must be explored in a time-constrained manner. In this work, we present REMIX, the first system to address the problem of outlier detection in an interactive setting. REMIX uses a novel mixed integer programming (MIP) formulation for automatically selecting and executing a diverse set of outlier detectors within a time limit. This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors. REMIX provides two distinct ways for the analyst to consume its results: (i) a partitioning of the detectors explored by REMIX into perspectives through low-rank non-negative matrix factorization; each perspective can be easily visualized as an intuitive heatmap of experiments versus outliers, and (ii) an ensembled set of outliers which combines outlier scores from all detectors. We demonstrate the benefits of REMIX through extensive empirical validation on real-world data.

The study of career development has become more important during a time of rising competition. Even with the help of newly available big data in the field of human resources, it is challenging to prospect the career development of talents in an effective manner, since the nature and structure of talent careers can change quickly. To this end, in this paper, we propose a novel survival analysis approach to model the talent career paths, with a focus on two critical issues in talent management, namely turnover and career progression. Specifically, for modeling the talent turnover behaviors, we formulate the prediction of survival status at a sequence of time intervals as a multi-task learning problem by considering the prediction at each time interval as a task. Also, we impose the ranking constraints to model both censored and uncensored data, and capture the intrinsic properties exhibited in general lifetime modeling with non-recurrent and recurrent events. Similarly, for modeling the talent career progression, each task concerns the prediction of a relative occupational level at each time interval. The ranking constraints imposed on different occupational levels can help to reduce the prediction error. Finally, we evaluate our approach with several state-of-the-art baseline methods on real-world talent data. The experimental results clearly demonstrate the effectiveness of the proposed models for predicting the turnover and career progression of talents.

The mobile in-App service analysis, aiming at classifying mobile internet traffic into different types of service usages, has become a challenging and emergent task for mobile service providers due to the increasing adoption of secure protocols for in-App services. While some efforts have been made for the classification of mobile internet traffic, existing methods rely on complex feature construction and large storage cache, which lead to low processing speed, and thus not practical for online real-time scenarios. To this end, we develop an iterative analyzer for classifying encrypted mobile traffic in a real-time way. Specifically, we first select an optimal set of most discriminative features from raw features extracted from traffic packet sequences by a novel Maximizing Inner activity similarity and Minimizing Different activity similarity (MIMD) measurement. To develop the online analyzer, we first represent a traffic flow with a series of time windows, which are described by the optimal feature vector and are updated iteratively at the packet level. Instead of extracting feature elements from a series of raw traffic packets, our feature elements are updated when a new traffic packet is observed and the storage of raw traffic packets is not required. The time windows generated from the same service usage activity are grouped by our proposed method, namely, recursive time continuity constrained KMeans clustering (rCKC). The feature vectors of cluster centers are then fed into a random forest classifier to identify corresponding service usages. Finally, we provide extensive experiments on real-world Internet traffic data from Wechat, Whatsapp, and Facebook to demonstrate the effectiveness and efficiency of our approach. The results show that the proposed analyzer provides high accuracy in real-world scenarios, and has low storage cache requirement as well as fast processing speed.

Point-of-Interest (POI) demand modeling in urban regions is critical for many applications such as business site selection and real estate investment. While some efforts have been made for the demand analysis of some specific POI categories, such as restaurants, it lacks systematic means to support POI demand modeling. To this end, in this paper, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data. Specifically, we first partition the urban space into spatially differentiated neighborhood regions formed by many small local communities. Then, the daily activity patterns of people traveling in the city will be extracted from human mobility data. Since the trip activities, even aggregated, are sparse and insufficient to directly identify the POI demands, especially for underdeveloped regions, we develop a latent factor model that integrates human mobility data, POI profiles, and demographic data to robustly model the POI demand of urban regions in a holistic way. In this model, POI preferences and supplies are used together with demographic features to estimate the POI demands simultaneously for all the urban regions interconnected in the city. Moreover, we also design efficient algorithms to optimize the latent model for large-scale data. Finally, experimental results on real-world data in New York City (NYC) show that our method is effective for identifying POI demands for different regions.

Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. Many providers of bike sharing systems are ready to expand their bike stations from the existing service area to surrounding regions. A key to success for a bike sharing systems expansion is the bike demand prediction for expansion areas. There are two major challenges in this demand prediction problem: First. the bike transition records are not available for the expansion area and second. station level bike demand have big variances across the urban city. Previous research efforts mainly focus on discovering global features, assuming the station bike demands react equally to the global features, which brings large prediction error when the urban area is large and highly diversified. To address these challenges, in this paper, we develop a hierarchical station bike demand predictor which analyzes bike demands from functional zone level to station level. Specifically, we first divide the studied bike stations into functional zones by a novel Bi-clustering algorithm which is designed to cluster bike stations with similar POI characteristics and close geographical distances together. Then, the hourly bike check-ins and check-outs of functional zones are predicted by integrating three influential factors: distance preference, zone-to-zone preference, and zone characteristics. The station demand is estimated by studying the demand distributions among the stations within the same functional zone. Finally, the extensive experimental results on the NYC Citi Bike system with two expansion stages show the advantages of our approach on station demand and balance prediction for bike sharing system expansions.

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.

We present an approach for improving the performance of complex knowledge-based processes by providing data-driven step-by-step recommendations. Our framework uses the associations between similar historic process performances and contextual information to determine the prototypical way of enacting the process. We introduce a novel similarity metric for grouping traces into clusters that incorporates temporal information about activity performance and handles concurrent activities. Our data-driven recommender system selects the appropriate prototype performance of the process based on user-provided context attributes. Our approach for determining the prototypes discovers the commonly performed activities and their temporal relationships. We tested our system on data from three real-world medical processes and achieved recommendation accuracy up to an F1 score of 0.77 (compared to an F1 score of 0.37 using ZeroR) with 63.2% of recommended enactments being within the first five neighbors of the actual historic enactments in a set of 87 cases. Our framework works as an interactive visual analytic tool for process mining. This work shows the feasibility of data-driven decision support system for complex knowledge-based processes.

Matrix sketching is aimed at finding compact representations of a matrix while simultaneously preserving most of its properties, which is a fundamental building block in modern scientific computing. Randomized algorithms represent state-of-the-art and have attracted huge interest from the fields of machine learning, data mining, and theoretic computer science. However, it still requires the use of the entire input matrix in producing desired factorizations, which can be a major computational and memory bottleneck in truly large problems. In this paper, we uncover an interesting theoretic connection between matrix low-rank decomposition and lossy signal compression, based on which a cascaded compression sampling framework is devised to approximate an m-by-n matrix in only O(m+n) time and space. Indeed, the proposed method accesses only a small number of matrix rows and columns, which significantly improves the memory footprint. Meanwhile, by sequentially teaming two rounds of approximation procedures and upgrading the sampling strategy from a uniform probability to more sophisticated, encoding-orientated sampling, significant algorithmic boosting is achieved to uncover more granular structures in the data. Empirical results on a wide spectrum of real-world, large-scale matrices show that by taking only linear time and space, the accuracy of our method rivals those state-of-the-art randomized algorithms consuming a quadratic, O(mn), amount of resources.

In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as ground-truth for each user. We then represent each user or item as a vector resulted from the factorization of the user-item rating matrix. In our paper, we try to discover a factorization for matching the following supervised learning task. In doing this, we define two coupled optimization problems, parameterized matrix factorization and structural learning, to formulate our task. And we propose a diversified collaborative filtering algorithm (DCF) to solve the coupled problems. We also introduce a new pairwise accuracy metric and a normalized topic coverage diversity metric to measure the performance of accuracy and diversity respectively. Extensive experiments on benchmark datasets show the performance gains of DCF in comparison with the state-of-the-art algorithms.

Product bundling is a marketing strategy that offers several products/items for sale as one bundle. While the bundling strategy has been widely used, less efforts have been made to understand how items should be bundled with respect to consumers’ preferences and buying motives for product bundles. This article investigates the relationships between the items that are bought together within a product bundle. To that end, each purchased product bundle is formulated as a bundle graph with items as nodes and the associations between pairs of items in the bundle as edges. The relationships between items can be analyzed by the formation of edges in bundle graphs, which can be attributed to the associations of feature aspects. Then, a probabilistic model BPM (Bundle Purchases with Motives) is proposed to capture the composition of each bundle graph, with two latent factors node-type and edge-type introduced to describe the feature aspects and relationships respectively. Furthermore, based on the preferences inferred from the model, an approach for recommending items to form product bundles is developed by estimating the probability that a consumer would buy an associative item together with the item already bought in the shopping cart. Finally, experimental results on real-world transaction data collected from well-known shopping sites show the effectiveness advantages of the proposed approach over other baseline methods. Moreover, the experiments also show that the proposed model can explain consumers’ buying motives for product bundles in terms of different node-types and edge-types .

For a long time, PageRank has been widely used for authority computation and has been adopted as a solid baseline for evaluating social influence related applications. However, when measuring the authority of network nodes, the traditional PageRank method does not take the nodes’ prior knowledge into consideration. Also, the connection between PageRank and social influence modeling methods is not clearly established. To that end, this article provides a focused study on understanding PageRank as well as the relationship between PageRank and social influence analysis. Along this line, we first propose a linear social influence model and reveal that this model generalizes the PageRank-based authority computation by introducing some constraints. Then, we show that the authority computation by PageRank can be enhanced if exploiting more reasonable constraints (e.g., from prior knowledge). Next, to deal with the computational challenge of linear model with general constraints, we provide an upper bound for identifying nodes with top authorities. Moreover, we extend the proposed linear model for better measuring the authority of the given node sets, and we also demonstrate the way to quickly identify the top authoritative node sets. Finally, extensive experimental evaluations on four real-world networks validate the effectiveness of the proposed linear model with respect to different constraint settings. The results show that the methods with more reasonable constraints can lead to better ranking and recommendation performance. Meanwhile, the upper bounds formed by PageRank values could be used to quickly locate the nodes and node sets with the highest authorities.

Advances in real-time location systems have enabled us to collect massive amounts of fine-grained semantically rich location traces, which provide unparalleled opportunities for understanding human activities and generating useful knowledge. This, in turn, delivers intelligence for real-time decision making in various fields, such as workflow management. Indeed, it is a new paradigm to model workflows through knowledge discovery in location traces. To that end, in this paper, we provide a focused study of workflow modeling by integrated analysis of indoor location traces in the hospital environment. In particular, we develop a workflow modeling framework that automatically constructs the workflow states and estimates the parameters describing the workflow transition patterns. More specifically, we propose effective and efficient regularizations for modeling the indoor location traces as stochastic processes. First, to improve the interpretability of the workflow states, we use the geography relationship between the indoor rooms to define a prior of the workflow state distribution. This prior encourages each workflow state to be a contiguous region in the building. Second, to further improve the modeling performance, we show how to use the correlation between related types of medical devices to reinforce the parameter estimation for multiple workflow models. In comparison with our preliminary work [11], we not only develop an integrated workflow modeling framework applicable to general indoor environments, but also improve the modeling accuracy significantly. We reduce the average log-loss by up to 11 percent.

Transfer learning has been proven to be effective for the problems where training data from a source domain and test data from a target domain are drawn from different distributions. To reduce the distribution divergence between the source domain and the target domain, many previous studies have been focused on designing and optimizing objective functions with the Euclidean distance to measure dissimilarity between instances. However, in some real-world applications, the Euclidean distance may be inappropriate to capture the intrinsic similarity or dissimilarity between instances. To deal with this issue, in this paper, we propose a metric transfer learning framework (MTLF) to encode metric learning in transfer learning. In MTLF, instance weights are learned and exploited to bridge the distributions of different domains, while Mahalanobis distance is learned simultaneously to maximize the intra-class distances and minimize the inter-class distances for the target domain. Unlike previous work where instance weights and Mahalanobis distance are trained in a pipelined framework that potentially leads to error propagation across different components, MTLF attempts to learn instance weights and a Mahalanobis distance in a parallel framework to make knowledge transfer across domains more effective. Furthermore, we develop general solutions to both classification and regression problems on top of MTLF, respectively. We conduct extensive experiments on several real-world datasets on object recognition, handwriting recognition, and WiFi location to verify the effectiveness of MTLF compared with a number of state-of-the-art methods.

In marketing analytics, customer segmentation (clustering) divides a customer base into groups of similar individuals, while buyer targeting (classification) identifies promising customers. Both customer segmentation and buyer targeting help the business to improve marketing performances by allocating resources to the most profitable customers. Due to the heterogeneity across the customer groups, some studies have been made on combining the tasks of customer segmentation and buyer targeting for tailored marketing strategies. However, these efforts usually combine these two tasks in a simple step-by-step approach. It is still unclear how to implement these two tasks in a more integrated and optimized way, which is the research objective of this paper. Specifically, we formulate customer segmentation and buyer targeting as a unified optimization problem. Then, the customer segments are adaptively realized during the targeting optimization process. In this way, the integrated approach not only improves the buyer targeting performances but also provides a new perspective of segmentation based on the buying decision preferences of the customers. The unified customer segmentation and buyer targeting method not only quantifies the purchase tendency of a specific customer but also characterizes the buying decision behaviors at the segment level. We also develop an efficient K-Classifiers Segmentation algorithm to solve the unified optimization problem. Moreover, we show that the customer segmentation based on the buying decision preferences can also be consistent with the features on customer profiles. Finally, we have performed the extensive experiments on several real-world Business to Business (B2B) marketing data sets. The results show that our approach offers not only more accurate targeting of promising customers but also meaningful customer segmentation solutions with interpretable buying decision preferences for each customer segment.

In the past decade, there have been a large number of transfer learning algorithms proposed for various real-world applications. However, most of them are vulnerable to negative transfer since their performance is even worse than traditional supervised models. Aiming at more robust transfer learning models, we propose an ENsemble framework of anCHOR adapters (ENCHOR for short), in which an anchor adapter adapts the features of instances based on their similarities to a specific anchor (i.e., a selected instance). Specifically, the more similar to the anchor instance, the higher degree of the original feature of an instance remains unchanged in the adapted representation, and vice versa. This adapted representation for the data actually expresses the local structure around the corresponding anchor, and then any transfer learning method can be applied to this adapted representation for a prediction model, which focuses more on the neighborhood of the anchor. Next, based on multiple anchors, multiple anchor adapters can be built and combined into an ensemble for final output. Additionally, we develop an effective measure to select the anchors for ensemble building to achieve further performance improvement. Extensive experiments on hundreds of text classification tasks are conducted to demonstrate the effectiveness of ENCHOR. The results show that: when traditional supervised models perform poorly, ENCHOR (based on only 8 selected anchors) achieves $6%-13%$ increase in terms of average accuracy compared with the state-of-the-art methods, and it greatly alleviates negative transfer.

The service usage analysis, aiming at identifying customers' messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced frame-work for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage sub-sequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach.

The prediction of academic performance is one of the most important tasks in educational data mining, and has been widely studied in MOOCs and intelligent tutoring systems. Academic performance could be affected with factors like personality, skills, social environment, the use of library books and so on. However, it is still less investigated that how could the use of library books affect academic performance of college students and even leverage book-loan history for predicting academic performance. To this end, we propose a supervised content-aware matrix factorization for mutual reinforcement of academic performance prediction and library book recommendation. This model not only addresses the sparsity challenge by explainable dimension reduction techniques, but also promotes library book recommendation by recommending "right" books for students based on their performance levels and book meta information. Finally, we evaluate the proposed model on three years of the book-loan history and cumulative grade point average of 13,047 undergraduate students in one university. The results show that the proposed model outperforms the competing baselines on both tasks, and that academic performance is not only predictable from the book-loan history but also improves the recommendation of library books for students.

Point of interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). However, quite different from traditional interest-oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: we need to examine whether the POI fits a user's availability. While there are some prior studies which included the temporal effect into POI recommendations, they overlooked the compatibility between time-varying popularity of POIs and regular availability of users, which we believe has a non-negligible impact on user decision-making. To this end, in this paper, we present a novel method which incorporates the degree of temporal matching between users and POIs into personalized POI recommendations. Specifically, we first profile the temporal popularity of POIs to show when a POI is popular for visit by mining the spatio-temporal human mobility and POI category data. Secondly, we propose latent user regularities to characterize when a user is regularly available for exploring POIs, which is learned with a user-POI temporal matching function. Finally, results of extensive experiments with real-world POI check-in and human mobility data demonstrate that our proposed user-POI temporal matching method delivers substantial advantages over baseline models for POI recommendation tasks.

Recent years have witnessed the boom of venture capital industry. Venture capitalists can attain great financial rewards if their invested companies exit successfully, via being acquired or going IPO (Initial Public Offering). The literature has revealed that, from both financial and managerial perspectives, decision-making process and successful rates of venture capital (VC) investments can be greatly improved if the investors well know the team members of target startups. However, much less efforts have been made on understanding the impact of prominent social ties between the members of VC firms and start-up companies on investment decisions. To this end, we propose to study such social relationship and see how this information can contribute to foreseeing investment deals. We aim at providing analytical guidance for the venture capitalists in choosing right investment targets. Specifically, we develop a Social-Adjusted Probabilistic Matrix Factorization (PMF) model to exploit members social connections information from VC firms and startups for investment recommendations. Unlike previous studies, we make use of the directed relationship between any pair of connected members from the two institutions respectively and quantify the variety of social network groups. As a result, it brings in much more flexibility, and the modeling results inherently provide meaningful managerial implications for the operators of VC firms and startups. Finally, we evaluate our model on both synthetic and real-world data. The results demonstrate that our approach outperforms the baseline algorithms with a significant margin.

Massive data collected by automated fare collection (AFC) systems provide opportunities for studying both personal traveling behaviors and collective mobility patterns in the urban area. Existing studies on the AFC data have primarily focused on identifying passengers' movement patterns. In this paper, however, we creatively leveraged such data for identifying thieves in the public transit systems. Indeed, stopping pickpockets in the public transit systems has been critical for improving passenger satisfaction and public safety. However, it is challenging to tell thieves from regular passengers in practice. To this end, we developed a suspect detection and surveillance system, which can identify pick-pocket suspects based on their daily transit records. Specifically, we first extracted a number of features from each passenger's daily activities in the transit systems. Then, we took a two-step approach that exploits the strengths of unsupervised outlier detection and supervised classification models to identify thieves, who exhibit abnormal traveling behaviors. Experimental results demonstrated the effective- ness of our method. We also developed a prototype system with a user-friendly interface for the security personnel.

Point-of-interest (POI) recommendation, which helps mobile users explore new places, has become an important location-based service. Existing approaches for POI recommendation have been mainly focused on exploiting the information about user preferences, social influence, and geographical influence. However, these approaches cannot handle the scenario where users are expecting to have POI recommendation for a specific time period. To this end, in this paper, we propose a unified recommender system, named the 'Where and When to gO' (WWO) recommender system, to integrate the user interests and their evolving sequential preferences with temporal interval assessment. As a result, the WWO system can make recommendations dynamically for a specific time period and the traditional POI recommender system can be treated as the special case of the WWO system by setting this time period long enough. Specifically, to quantify users' sequential preferences, we consider the distributions of the temporal intervals between dependent POIs in the historical check-in sequences. Then, to estimate the distributions with only sparse observations, we develop the low-rank graph construction model, which identifies a set of bi-weighted graph bases so as to learn the static user preferences and the dynamic sequential preferences in a coherent way. Finally, we evaluate the proposed approach using real-world data sets from several location-based social networks (LBSNs). The experimental results show that our method outperforms the state-of-the-art approaches for POI recommendation in terms of various metrics, such as F-measure and NDCG, with a significant margin.

Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. A key to success for a bike sharing systems is the effectiveness of rebalancing operations, that is, the efforts of restoring the number of bikes in each station to its target value by routing vehicles through pick-up and drop-off operations. There are two major issues for this bike rebalancing problem: the determination of station inventory target level and the large scale multiple capacitated vehicle routing optimization with outlier stations. The key challenges include demand prediction accuracy for inventory target level determination, and an effective optimizer for vehicle routing with hundreds of stations. To this end, in this paper, we develop a Meteorology Similarity Weighted K-Nearest-Neighbor (MSWK) regressor to predict the station pick-up demand based on large-scale historic trip records. Based on further analysis on the station network constructed by station-station connections and the trip duration, we propose an inter station bike transition (ISBT) model to predict the station drop-off demand. Then, we provide a mixed integer nonlinear programming (MINLP) formulation of multiple capacitated bike routing problem with the objective of minimizing total travel distance. To solve it, we propose an Adaptive Capacity Constrained K-centers Clustering (AdaCCKC) algorithm to separate outlier stations (the demands of these stations are very large and make the optimization infeasible) and group the rest stations into clusters within which one vehicle is scheduled to redistribute bikes between stations. In this way, the large scale multiple vehicle routing problem is reduced to inner cluster one vehicle routing problem with guaranteed feasible solutions. Finally, the extensive experimental results on the NYC Citi Bike system show the advantages of our approach for bike demand prediction and large-scale bike rebalancing optimization.

The analysis of large-scale Electrical Medical Records (EMRs) has the potential to develop and optimize clinical treatment regimens. A treatment regimen usually includes a series of doctor orders containing rich temporal and heterogeneous information. However, in many existing studies, a doctor order is simplified as an event code and a treatment record is simplified as a code sequence. Thus, the information inherent in doctor orders is not fully used for in-depth analysis. In this paper, we aim at exploiting the rich information in doctor orders and developing data-driven approaches for improving clinical treatments. To this end, we first propose a novel method to measure the similarities between treatment records with consideration of sequential and multifaceted information in doctor orders. Then, we propose an efficient density-based clustering algorithm to summarize large-scale treatment records, and extract a semantic representation of each treatment cluster. Finally, we develop a unified framework to evaluate the discovered treatment regimens, and find the most effective treatment regimen for new patients. In the empirical study, we validate our methods with EMRs of 27,678 patients from 14 hospitals. The results show that: 1) Our method can successfully extract typical treatment regimens from large-scale treatment records. The extracted treatment regimens are intuitive and provide managerial implications for treatment regimen design and optimization. 2) By recommending the most effective treatment regimens, the total cure rate in our data improves from 19.89% to 21.28%, and the effective rate increases up to 98.29%.

With the high mobility of talent, it becomes critical for the recruitment team to find the right talent from the right source in an efficient manner. The prevalence of Online Professional Networks (OPNs), such as LinkedIn, enables the new paradigm for talent recruitment and job search. However, the dynamic and complex nature of such talent information imposes significant challenges to identify prospective talent sources from large-scale professional networks. Therefore, in this paper, we propose to create a job transition network where vertices stand for organizations and a directed edge represents the talent flow between two organizations for a time period. By analyzing this job transition network, it is able to extract talent circles in a way such that every circle includes the organizations with similar talent exchange patterns. Then, the characteristics of these talent circles can be used for talent recruitment and job search. To this end, we develop a talent circle detection model and design the corresponding learning method by maximizing the Normalized Discounted Cumulative Gain (NDCG) of inferred probability for the edge existence based on edge weights. Then, the identified circles will be labeled by the representative organizations as well as keywords in job descriptions. Moreover, based on these identified circles, we develop a talent exchange prediction method for talent recommendation. Finally, we have performed extensive experiments on real-world data. The results show that, our method can achieve much higher modularity when comparing to the benchmark approaches, as well as high precision and recall for talent exchange prediction.

With recent advances in mobile and sensor technologies, a large amount of efforts have been made on developing intelligent applications for taxi drivers, which provide beneficial guide and opportunity to improve the profit and work efficiency. However, limited scopes focus on the latent social interaction within cab drivers, and corresponding social propagation scheme to share driving behaviors has been largely ignored. To that end, in this paper, we propose a comprehensive study to reveal how the social propagation affects for better prediction of cab drivers' future behaviors. To be specific, we first investigate the correlation between drivers' skills and their mutual interactions in the latent vehicle-to-vehicle network, which intuitively indicates the effects of social influences. Along this line, by leveraging the classic social influence theory, we develop a two-stage framework for quantitatively revealing the latent driving pattern propagation within taxi drivers. Comprehensive experiments on a real-word data set collected from the New York City clearly validate the effectiveness of our proposed framework on predicting future taxi driving behaviors, which also support the hypothesis that social factors indeed improve the predictability of driving behaviors.

Days on Market (DOM) refers to the number of days a property is on the active market, which is an important measurement of market liquidity in real estate industry. Indeed, at the micro level, DOM is not only a special concern of house sellers, but also a useful indicator for potential buyers to evaluate the popularity of a house. At the macro level, DOM is an important indicator of real estate market status. However, it is very challenging to measure DOM, since there are a variety of factors which can impact on the DOM of a property. To this end, in this paper, we aim to measure real estate liquidity by examining multiple factors in a holistic manner. A special goal is to predict the DOM of a given property listing. Specifically, we first extract key features from multiple types of heterogeneous real estate-related data, such as house profiles and geo-social information of residential communities. Then, based on these features, we develop a multi-task learning based regression approach for predicting the DOM of real estates. This approach can effectively learn district-aware models for different property listings by considering multiple factors. Finally, we conduct extensive experiments on real-world real estate data collected in Beijing and develop a prototype system for practical use. The experimental results clearly validate the effectiveness of the proposed approach for measuring liquidity in real estate markets.

Recruitment market analysis provides valuable understanding of industry-specific economic growth and plays an important role for both employers and job seekers. With the rapid development of online recruitment services, massive recruitment data have been accumulated and enable a new paradigm for recruitment market analysis. However, traditional methods for recruitment market analysis largely rely on the knowledge of domain experts and classic statistical models, which are usually too general to model large-scale dynamic recruitment data, and have difficulties to capture the fine-grained market trends. To this end, in this paper, we propose a new research paradigm for recruitment market analysis by leveraging unsupervised learning techniques for automatically discovering recruitment market trends based on large-scale recruitment data. Specifically, we develop a novel sequential latent variable model, named MTLVM, which is designed for capturing the sequential dependencies of corporate recruitment states and is able to automatically learn the latent recruitment topics within a Bayesian generative framework. In particular, to capture the variability of recruitment topics over time, we design hierarchical dirichlet processes for MTLVM. These processes allow to dynamically generate the evolving recruitment topics. Finally, we implement a prototype system to empirically evaluate our approach based on real-world recruitment data in China. Indeed, by visualizing the results from MTLVM, we can successfully reveal many interesting findings, such as the popularity of LBS related jobs reached the peak in the 2nd half of 2014, and decreased in 2015.

Recent years have witnessed the unprecedented growth of online social media, which empower short texts as the prevalent format for information of Internet. Given the nature of sparsity, however, short text topic modeling remains a critical yet much-watched challenge in both academy and industry. Rich research efforts have been put on building different types of probabilistic topic models for short texts, among which the self aggregation methods without using auxiliary information become an emerging solution for providing informative cross-text word co-occurrences. However, models along this line are still rarely seen, and the representative one Self-Aggregation Topic Model (SATM) is prone to overfitting and computationally expensive. In light of this, in this paper, we propose a novel probabilistic model called Pseudo-document-based Topic Model (PTM) for short text topic modeling. PTM introduces the concept of pseudo document to implicitly aggregate short texts against data sparsity. By modeling the topic distributions of latent pseudo documents rather than short texts, PTM is expected to gain excellent performance in both accuracy and efficiency. A Sparsity-enhanced PTM (SPTM for short) is also proposed by applying Spike and Slab prior, with the purpose of eliminating undesired correlations between pseudo documents and latent topics. Extensive experiments on various real-world data sets with state-of-the-art baselines demonstrate the high quality of topics learned by PTM and its robustness with reduced training samples. It is also interesting to show that i) SPTM gains a clear edge over PTM when the number of pseudo documents is relatively small, and ii) the constraint that a short text belongs to only one pseudo document is critically important for the success of PTM. We finally take an in-depth semantic analysis to unveil directly the fabulous function of pseudo documents in finding cross-text word co-occurrences for topic modeling.

In online karaoke, the decision process in choosing a song is different from that in music radio, because users usually prefer songs that meet their vocal competence besides their tastes. Traditional music recommendation methods typically model users' personalized preference for songs in terms of content and style. However, this can be improved by considering the degree of matching the vocal competence (e.g. pitch, volume, and rhythm) of users to the vocal requirements of songs. To this end, in this paper, we develop a karaoke recommender system by incorporating vocal competence. Along this line, we propose a joint modeling method named CBNTF by exploiting the mutual enhancement between non-negative tensor factorization (NTF) and support vector machine (SVM). Specifically, we first extract vocal (i.e., pitch, volume, and rhythm) ratings of a user for a song from his/her singing records. Since these vocal ratings encode users' vocal competence from three aspects, we treat these vocal ratings as a tensor, exploit an NTF method, and learn the latent features of users' vocal metrics. These factorized features are simultaneously fed into an SVM classifier and then we use the trained classifier to predict the overall rating of a user with respect to a song. In addition, we propose an enhanced objective function to exploit the mutual enhancement between NTF and SVM, and devise an effective method to solve this objective as a coupled least-squares optimization problem via a maximum margin framework. With the estimated model, we compute the similarity between users and songs in terms of pitch, volume and rhythm and recommend songs to users. Finally, we conduct extensive experiments with real-world online karaoke data. The results demonstrate the effectiveness of our method.

It is well recognized that community safety which affects people's right to live without fear of crime has considerable impacts on housing investments. Housing investors can make more informed decisions if they are fully aware of safety related factors. To this end, we develop a safety-aware house ranking method by incorporating community safety into house assessment. Specifically, we first propose a novel framework to infer community safety level by mining community crime evidences from rich spatio-temporal historical crime data. Then we develop a ranking model which fuses multiply community safety features to rank house value based on the degree of community safety. Finally, we conduct a comprehensive evaluation of the proposed method with real-world crime and house data. The experimental results show that the proposed method substantially outperforms the baseline methods for house ranking.

It is traditionally a challenge for home buyers to understand, compare, and contrast the investment value of real estate. Although a number of appraisal methods have been developed to value real properties, the performances of these methods have been limited by traditional data sources for real estate appraisal. With the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of real estate for enhancing real estate appraisal. Indeed, the geographic dependencies of the investment value of an estate can be from the characteristics of its own neighborhood (individual), the values of its nearby estates (peer), and the prosperity of the affiliated latent business area (zone). To this end, in this paper, we propose a geographic method, named ClusRanking, for real estate appraisal by leveraging the mutual enforcement of ranking and clustering power. ClusRanking is able to exploit geographic individual, peer, and zone dependencies in a probabilistic ranking model. Specifically, we first extract the geographic utility of estates from geography data, estimate the neighborhood popularity of estates by mining taxicab trajectory data, and model the influence of latent business areas. Also, we fuse these three influential factors and predict real estate investment value. Moreover, we simultaneously consider individual, peer and zone dependencies, and derive an estate-specific ranking likelihood as the objective function. Furthermore, we propose an improved method named CR-ClusRanking by incorporating checkin information as a regularization term which reduces the performance volatility of real estate ranking system. Finally, we conduct a comprehensive evaluation with the real estate-related data of Beijing, and the experimental results demonstrate the effectiveness of our proposed methods.

Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in application stores has imposed the challenge of finding the right apps to meet the user needs. Indeed, there is a critical demand for personalized app recommendations. Along this line, there are opportunities and challenges posed by two unique characteristics of mobile apps. First, app markets have organized apps in a hierarchical taxonomy. Second, apps with similar functionalities are competing with each other. Although there are a variety of approaches for mobile app recommendations, these approaches do not have a focus on dealing with these opportunities and challenges. To this end, in this article, we provide a systematic study for addressing these challenges. Specifically, we develop a structural user choice model (SUCM) to learn fine-grained user preferences by exploiting the hierarchical taxonomy of apps as well as the competitive relationships among apps. Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. Finally, we perform extensive experiments on a large app adoption dataset collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art Top-N recommendation methods by a significant margin.

Sequential pattern analysis aims at finding statistically relevant temporal structures where the values are delivered in a sequence. With the growing complexity of real-world dynamic scenarios, more and more symbols are often needed to encode the sequential values. This is so-called “curse of cardinality”, which can impose significant challenges to the design of sequential analysis methods in terms of computational efficiency and practical use. Indeed, given the overwhelming scale and the heterogeneous nature of the sequential data, new visions and strategies are needed to face the challenges. To this end, in this paper, we propose a “temporal skeletonization” approach to proactively reduce the cardinality of the representation for sequences by uncovering significant, hidden temporal structures. The key idea is to summarize the temporal correlations in an undirected graph, and use the “skeleton” of the graph as a higher granularity on which hidden temporal patterns are more likely to be identified. As a consequence, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data. Our approach has shown to greatly alleviate the curse of cardinality in challenging tasks of sequential pattern mining and clustering. Evaluation on a business-to-business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy customer event data.

Advances in wireless communication technology have enabled information exchange opportunities between moving vehicles within proximity. Potentially through such physical contacts a piece of information can diffuse to the entire network. While there has been extensive research on information diffusion in social networks, we do not know much about the spatial patterns in vehicle motion and how such patterns can support information dissemination. To this end, in this paper, we provide a systematic study of three large-scale data sets of taxi GPS traces from three big cities. The study shows the following properties universal of the three data sets: 1) the small world property, that information can be disseminated to almost the entire set of participants, within a very small number of hops; 2) certain physical contacts can be extremely effective in exchanging messages and such effectiveness shows a power law distribution; 3) the lack of hubs, no vehicle behaves as major hubs; removing top 20% nodes that have the highest number of physical contacts does not affect the effectiveness of information dissemination. 4) the information dissemination exhibits strong spatial temporal correlation. Finally, to explain the observations in particular the small world property, we develop mathematical models of the taxi movement patterns such that on graph topologies exhibiting properties of real-world road networks a number of observations can be rigorously proved.

College students with financial difficulties refer to those whose families can hardly afford their high tuition in universities, and should be supported by modern funding system. Indeed, students' economic plight negatively impact their mental health, academic performance, as well as their personal and social life. While funding students in financial hardship is widely accepted, there is limited understanding and research on effectively identification of the qualifying students. Traditional approaches relying on advisers' personal assessments are inefficient, and such subjective judgements may not reflect the truth. To this end, in this paper, we explore the data mining techniques for identifying students who are qualified for financial support. Specifically, we investigate students' complex behaviors on campus from multiple perspectives, and develop a learning framework, named Dis-HARD, by jointly incorporating the heterogeneous features to predict the portfolio of stipends a given student should be awarded. Our framework formalizes the above problem as a multi-label learning problem. Along this line, we first extract discriminative features from three perspectives: (i) smartcard usage behavior, (ii) internet usage behavior and (iii) trajectory on campus. Then, we develop a linear loss function with regularization to solve this multi-label classification problem. In addition, to effectively exploit the students' similarity and label dependency, we incorporate the graph Laplacian and composite l2,1-norm into the regularization of our model, and develop are-weighted algorithm to achieve effective optimization. Finally, experiments on real-world data demonstrate that our method consistently provides better performance compared to the existing state-of-the-art methods.

Bike sharing systems, aiming at providing the missing links in the public transportation systems, are becoming popular in urban cities. In an ideal bike sharing network, the station locations are usually selected in a way that there are balanced pick-ups and drop-offs among stations. This can help avoid expensive re-balancing operations and maintain high user satisfaction. However, it is a challenging task to develop such an efficient bike sharing system with appropriate station locations. Indeed, the bike station demand is influenced by multiple factors of surrounding environment and complex public transportation networks. Limited efforts have been made to develop demand-and-balance prediction models for bike sharing systems by considering all these factors. To this end, in this paper, we propose a bike sharing network optimization approach by considering multiple influential factors. The goal is to enhance the quality and efficiency of the bike sharing service by selecting the right station locations. Along this line, we first extract fine-grained discriminative features from human mobility data, point of interests (POI), as well as station network structures. Then, prediction models based on Artificial Neural Networks (ANN) are developed for predicting station demand and balance. In addition, based on the learned patterns of station demand and balance, a genetic algorithm based optimization model is built to choose a set of stations from a large number of candidates in a way such that the station usage is maximized and the number of unbalanced stations is minimized. Finally, the extensive experimental results on the NYC CitiBike sharing system show the advantages of our approach for optimizing the station site allocation in terms of the bike usage as well as the required re-balancing efforts.

In the retail market, the consumers' indecisiveness refers to the inability to make quick and assertive decisions when they choose among competing product options. Indeed, indecisiveness has been investigated in a number of fields, such as economics and psychology. However, these studies are usually based on the subjective customer survey data with some manually defined questions. Instead, in this paper, we provide a focused study on automatically mining indecisiveness in massive customer behaviors in online stores. Specifically, we first give a general definition to measure the observed indecisiveness in each behavior session. From these observed indecisiveness, we can learn the latent factors/reasons by a probabilistic factor-based model. These two factors are the indecisive indexes of the customers and the product bundles, respectively. Next, we demonstrate that this indecisiveness mining process could be useful in several potential applications, such as the competitive product detection and personalized product bundles recommendation. Finally, we perform extensive experiments on a large-scale behavioral logs of online customers in a distributed environment. The results reveal that our measurement of indecisiveness agrees with the common sense assessment, and the discoveries are useful in predicting customer behaviors and providing better recommendation services for both customers and online retailers.

Patent data has been used for generating patent-based indicators for tracking the technology development of high-tech companies. In this paper, we further show the promises of exploiting patent data for the analysis and prospecting of high-tech companies in the stock market. Specifically, we aim at investigating the relationship between the patent activities of high-tech companies and the dynamics of their stock price movement. While stock forecasting is a topic of general interest and has been studied extensively in the literature, the most popular forecasting models do not facilitate the discovery of the patent-activity impact all essential characteristics of the market performance of a given stock. To this end, we propose a new approach to analyze the relationships between patent activities and the statistical characteristics of stock prices. To the best of our knowledge, we are the first to propose a model of this nature, relating patent data mining and financial modeling. Also, we demonstrate the relationships of the market-adjusted stock returns and the number of patent applications as well as the diversity of the corresponding patent categories. Moreover, we establish relationships between the monthly drift and volatility of the market-adjusted stock returns and those patent activity indicators. Here, we exploit a widely accepted diffusion model of the stock returns and estimate its parameters. By adopting the moving window technique, we create fitted models by introducing various lagged terms of patent activity characteristics. For each company, we consider the coefficients of each significant term over the entire time horizon and perform further statistical testing on the overall significance of the corresponding indicator. The analysis has been performed on real-world stock trading data as well as patent data. The results confirm the significant impact of patent activity on stock movement and on its essential statistical characteristics of drift and volatility.

Discovering the determinants of job change and predicting the individual job change occasion are essential approaches for understanding the professional careers of human. However, with the evolution of labor division and globalization, modern careers become more self-directed and dynamic, which makes job change occasion difficult to predict. Fortunately, the emerging online professional networks and location-based social networks provide a large amount of work experience and daily activity records of individuals around the world, which open a venue for the accurate job change analysis. Discovering the determinants of job change and predicting the individual job change occasion are essential approaches for understanding the professional careers of human. However, with the evolution of labor division and globalization, modern careers become more self-directed and dynamic, which makes job change occasion difficult to predict. Fortunately, the emerging online professional networks and location-based social networks provide a large amount of work experience and daily activity records of individuals around the world, which open a venue for the accurate job change analysis. In this paper, we aggregate the work experiences and check-in records of individuals to model the job change motivations and correlations between professional and daily life. Specifically, we attempt to reveal to what extent the job change occasion can be predicted based on the career mobility and daily activity patterns at the individual level. Following the classical theory of job mobility determinants, we extract and quantify the environmental conditions and personal preference of careers from the perspective of industrial/regional constraints and personal interests/demands. Besides, we investigate the factors of activity patterns which may be correlated with job change as cause and effect results. First, we quantify the consumption diversity, sentiment fluctuation and geographic movement from the check-in records as indicators. Then, we leverage the center-bias level assignment and multi-point snapshot mechanism to capture historical and parallel migration. Finally, experimental results based on a large real-world dataset show that the job change occasions can be accurately predicted with the aggregated factors.

Business to Business (B2B) marketing aims at meeting the needs of other businesses instead of individual consumers. In B2B markets, the buying processes usually involve series of different marketing campaigns providing necessary information to multiple decision makers with different interests and motivations. The dynamic and complex nature of these processes imposes significant challenges to analyze the process logs for improving the B2B marketing practice. Indeed, most of the existing studies only focus on the individual consumers in the markets, such as movie/product recommender systems. In this paper, we exploit the temporal behavior patterns in the buying processes of the business customers and develop a B2B marketing campaign recommender system. Specifically, we first propose the temporal graph as the temporal knowledge representation of the buying process of each business customer. The key idea is to extract and integrate the campaign order preferences of the customer using the temporal graph. We then develop the low-rank graph reconstruction framework to identify the common graph patterns and predict the missing edges in the temporal graphs. We show that the prediction of the missing edges is effective to recommend the marketing campaigns to the business customers during their buying processes. Moreover, we also exploit the community relationships of the business customers to improve the performances of the graph edge predictions and the marketing campaign recommendations. Finally, we have performed extensive empirical studies on real-world B2B marketing data sets and the results show that the proposed method can effectively improve the quality of the campaign recommendations for challenging B2B marketing tasks.

Multi-task learning aims at learning multiple related but different tasks. In general, there are two ways for multi-task learning. One is to exploit the small set of labeled data from all tasks to learn a shared feature space for knowledge sharing. In this way, the focus is on the labeled training samples while the large amount of unlabeled data is not sufficiently considered. Another way has a focus on how to share model parameters among multiple tasks based on the original features space. Here, the question is whether it is possible to combine the advantages of both approaches and develop a method, which can simultaneously learn a shared subspace for multiple tasks and learn the prediction models in this subspace? To this end, in this paper, we propose a feature representation learning framework, which has the ability in combining the autoencoders, an effective way to learn good representation by using large amount of unlabeled data, and model parameter regularization methods into a unified model for multi-task learning. Specifically, all the tasks share the same encoding and decoding weights to find their latent feature representations, based on which a regularized multi-task softmax regression method is used to find a distinct prediction model for each task. Also, some commonalities are considered in the prediction models according to the relatedness of multiple tasks. There are several advantages of the proposed model: 1) it can make full use of large amount of unlabeled data from all the tasks to learn satisfying representations, 2) the learning of distinct prediction models can benefit from the success of autoencoder, 3) since we incorporate the labeled information into the softmax regression method, so the learning of feature representation is indeed in a semi-supervised manner. Therefore, our model is a semi-supervised autoencoder for multi-task learning (SAML for short). Finally, extensive experiments on three real-world data sets demonstrate the effectiveness of the proposed framework. Moreover, the feature representation obtained in this model can be used by other methods to obtain improved results.

Mixed land use refers to the effort of putting residential, commercial and recreational uses in close proximity to one another. This can contribute economic benefits, support viable public transit, and enhance the perceived security of an area. It is naturally promising to investigate how to rank real estate from the viewpoint of diverse mixed land use, which can be reflected by the portfolio of community functions in the observed area. To that end, in this paper, we develop a geographical function ranking method, named FuncDivRank, by incorporating the functional diversity of communities into real estate appraisal. Specifically, we first design a geographic function learning model to jointly capture the correlations among estate neighborhoods, urban functions, temporal effects, and user mobility patterns. In this way we can learn latent community functions and the corresponding portfolios of estates from human mobility data and Point of Interest (POI) data. Then, we learn the estate ranking indicator by simultaneously maximizing ranking consistency and functional diversity, in a unified probabilistic optimization framework. Finally, we conduct a comprehensive evaluation with real-world data. The experimental results demonstrate the enhanced performance of the proposed method for real estate appraisal.

The rapid growth in the development of healthcare information systems has led to an increased interest in utilizing the patient Electronic Health Records (EHR) for assisting disease diagnosis and phenotyping. The patient EHRs are generally longitudinal and naturally represented as medical event sequences, where the events include clinical notes, problems, medications, vital signs, laboratory reports, etc. The longitudinal and heterogeneous properties make EHR analysis an inherently difficult challenge. To address this challenge, in this paper, we develop a novel representation, namely the temporal graph, for such event sequences. The temporal graph is informative for a variety of challenging analytic tasks, such as predictive modeling, since it can capture temporal relationships of the medical events in each event sequence. By summarizing the longitudinal data, the temporal graphs are also robust and resistant to noisy and irregular observations. Based on the temporal graph representation, we further develop an approach for temporal phenotyping to identify the most significant and interpretable graph basis as phenotypes. This helps us better understand the disease evolving patterns. Moreover, by expressing the temporal graphs with the phenotypes, the expressing coefficients can be used for applications such as personalized medicine, disease diagnosis, and patient segmentation. Our temporal phenotyping framework is also flexible to incorporate semi-supervised/supervised information. Finally, we validate our framework on two real-world tasks. One is predicting the onset risk of heart failure. Another is predicting the risk of heart failure related hospitalization for patients with COPD pre-condition. Our results show that the diagnosis performance in both tasks can be improved significantly by the proposed approaches. Also, we illustrate some interesting phenotypes derived from the data.

Nowadays, it is commonly seen that an offline social event is organized through online social network services (SNS), in this way cyber strangers can be connected in physical world. While there are some preliminary studies on social event participation through SNS, they usually have more focus on the mining of event profiles and have less focus on the social relationships among target users. In particular, the importance of dynamic mutual influence among potential event participants has been largely ignored. In this paper, we develop a novel discriminant framework, which allows to integrate the dynamic mutual dependence of potential event participants into the discrimination process. Specifically, we formulate the group-oriented event participation problem as a variant two-stage discriminant framework to capture the users' preferences as well as their latent social connections. The experimental results on real-world data show that our method can effectively predict the event participation with a significant margin compared with several state-of-the-art baselines, which validates the hypothesis that dynamic mutual influence could play an important role in the decision-making process of social event participation.

The problem of Matrix Completion (MC) refers to the process of adding entries for unknown or missing values in a matrix. In this paper, we study the convex matrix completion problem in the form of trace norm bounding. Specifically, we propose a robust solution for this problem based on trace-ball optimization, which can creatively change the original trace norm constraint into the problem of low-rank matrix factorization. Therefore, by searching in a ball space defined by the new trace constraint, the rank of new matrix can be self-determined such that the local minimum for matrix factorization is the global minimum for the original matrix completion task. Meanwhile, we define a free parameter γ to control the model complexity of our approach in terms of how well it fits the training data. Particularly, we identify a value of γb, which is the minimal value of the trace norm, in a way such that the model can exactly fit the known entries in the matrix. Furthermore, we also empirically reveal an important property of our approach: that is, a variable η* generated by γ is always stable with the increase of the amount of training data. This can help to speed up the tuning of optimal parameters for large matrices. Finally, extensive experiments on several real-world datasets clearly validate the effectiveness of the proposed approach.

In this paper, we study the problem of personalized restaurant recommendations. Specifically, we develop a probabilistic factor analysis framework, named RMSQ-MF, which has the ability in exploiting multi-source information, such as the users' task, their friends' preferences, and human mobility patterns, for personalized restaurant recommendations. The rationale of this work is motivated by two observations. First, people's preferences can be affected by their friends. Second, human mobility patterns can reflect the popularity of restaurants to a certain degree. Finally, empirical studies on real-world data demonstrate that the proposed method outperforms benchmark methods with a significant margin.

Recent years have witnessed a rapid adoption of mobile devices and a dramatic proliferation of mobile applications (Apps for brevity). However, the large number of mobile Apps makes it difficult for users to locate relevant Apps. Therefore, recommending Apps becomes an urgent task. Traditional recommendation approaches focus on learning the interest of a user and the functionality of an item (e.g., an App) from a set of user-item ratings, and they recommend an item to a user if the item's functionality well matches the user's interest. However, Apps could have privileges to access a user's sensitive resources ( e.g., contact, message, and location). As a result, a user chooses an App not only because of its functionality, but also because it respects the user's privacy preference. To the best of our knowledge, this paper presents the first systematic study on incorporating both interest-functionality interactions and users' privacy preferences to perform personalized App recommendations. Specifically, we first construct a new model to capture the trade-off between functionality and user privacy preference. Then we crawled a real-world dataset (16,344 users, 6,157 Apps, and 263,054 ratings) from Google Play and use it to comprehensively evaluate our model and previous methods. We find that our method consistently and substantially outperforms the state-of-the-art approaches, which implies the importance of user privacy preference on personalized App recommendations. Moreover, we explore the impact of different levels of privacy information on the performances of our method, which gives us insights on what resources are more likely to be treated as private by users and influence users' behaviors at selecting Apps.

Frequent pattern mining is an important data mining problem with many broad applications. Most studies in this field use support (frequency) to measure the popularity of a pattern, namely the fraction of transactions or sequences that include the pattern in a data set. In this study, we introduce a new interesting measure, namely occupancy, to measure the completeness of a pattern in its supporting transactions or sequences. This is motivated by some real-world pattern recommendation applications in which an interesting pattern should not only be frequent, but also occupies a large portion of its supporting transactions or sequences. With the definition of occupancy we call a pattern dominant if its occupancy value is above a user-specified threshold. Then, our task is to identify the qualified patterns which are both dominant and frequent. Also, we formulate the problem of mining top-k qualified patterns , that is, finding k qualified patterns with maximum values on a user-defined function of support and occupancy, for example, weighted sum of support and occupancy. The challenge to these tasks is that the value of occupancy does not change monotonically when more items are appended to a given pattern. Therefore, we propose a general algorithm called DOFRA (DOminant and FRequent pattern mining Algorithm) for mining these qualified patterns, which explores the upper bound properties on occupancy to drastically reduce the search process. Finally, we show the effectiveness of DOFRA in two real-world applications and also demonstrate the efficiency of DOFRA on several real and large synthetic datasets.

The problem of point of interest (POI) recommendation is to provide personalized recommendations of places, such as restaurants and movie theaters. The increasing prevalence of mobile devices and of location based social networks (LBSNs) poses significant new opportunities as well as challenges, which we address. The decision process for a user to choose a POI is complex and can be influenced by numerous factors, such as personal preferences, geographical considerations, and user mobility behaviors. This is further complicated by the connection LBSNs and mobile devices. While there are some studies on POI recommendations, they lack an integrated analysis of the joint effect of multiple factors. Meanwhile, although latent factor models have been proved effective and are thus widely used for recommendations, adopting them to POI recommendations requires delicate consideration of the unique characteristics of LBSNs. To this end, in this paper, we propose a general geographical probabilistic factor model ($\sf{Geo}$-PFM) framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user’s check-in behavior. Also, user mobility behaviors can be effectively leveraged in the recommendation model. Moreover, based our$\sf{Geo}$-PFM framework, we further develop a Poisson$\sf{Geo}$-PFM which provides a more rigorous probabilistic generative process for the entire model and is effective in modeling the skewed user check-in count data as implicit feedback for better POI recommendations. Finally, extensive experimental results on three real-world LBSN datasets (which differ in terms of user mobility, POI geographical distribution, implicit response data skewness, and user-POI observation sparsity), show that the proposed recommendation methods outperform state-of-the-art latent factor models by a significant margin.

The objective of consensus clustering is to find a single partitioning which agrees as much as possible with existing basic partitionings. Consensus clustering emerges as a promising solution to find cluster structures from heterogeneous data. As an efficient approach for consensus clustering, the K-means based method has garnered attention in the literature, however the existing research efforts are still preliminary and fragmented. To that end, in this paper, we provide a systematic study of K-means-based consensus clustering (KCC). Specifically, we first reveal a necessary and sufficient condition for utility functions which work for KCC. This helps to establish a unified framework for KCC on both complete and incomplete data sets. Also, we investigate some important factors, such as the quality and diversity of basic partitionings, which may affect the performances of KCC. Experimental results on various realworld data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with many missing values.

The step of urbanization and modern civilization fosters different functional zones in a city, such as residential areas, business districts, and educational areas. In a metropolis, people commute between these functional zones every day to engage in different socioeconomic activities, e.g., working, shopping, and entertaining. In this paper, we propose a data-driven framework to discover functional zones in a city. Specifically, we introduce the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order. Later, we segment an urban area into disjointed regions according to major roads, such as highways and urban expressways. We have developed a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT. Furthermore, we identify the intensity of each functional zone using Kernel Density Estimation. Extensive experiments are conducted with several urban scale datasets to show that the proposed framework offers a powerful ability to capture city dynamics and provides valuable calibrations to urban planners in terms of functional zones.

Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of globalanomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps' ranking, rating and review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

Recent years have witnessed the rapid prevalence of online serials, which play an important role in our daily entertainment. A critical demand along this line is to predict the popularity of online serials, which can enable a wide range of applications, such as online advertising, and serial recommendation. However, compared with traditional online media such as user-generated content (UGC), online serials have unique characteristics of sequence dependence, release date dependence as well as unsynchronized update regularity. Therefore, the popularity prediction for online serials is a nontrivial task and still under-addressed. To this end, in this paper we present a comprehensive study for predicting the popularity of online serials with autoregressive models. Specifically, we first introduce a straightforward yet effective Naive Autoregressive (NAR) model based on the correlations of serial episodes. Furthermore, we develop a sophisticated model, namely Transfer Autoregressive (TAR) model, to capture the dynamic behaviors of audiences, which can achieve better prediction performance than the NAR model. Indeed, the two models can reveal the popularity generation from different perspectives. In addition, as a derivative of the TAR model, we also design a novel metric, namely favor, for evaluating the quality of online serials. Finally, extensive experiments on two real-world data sets clearly show that both models are effective and outperform baselines in terms of the popularity prediction for online serials. And the new metric performs better than other metrics for quality estimation.

Multi-task multi-view learning deals with the learning scenarios where multiple tasks are associated with each other through multiple shared feature views. All previous works for this problem assume that the tasks use the same set of class labels. However, in real world there exist quite a few applications where the tasks with several views correspond to different set of class labels. This new learning scenario is called Multi-task Multi-view Learning for Heterogeneous Tasks in this study. Then, we propose a Multi-tAsk MUlti-view Discriminant Analysis (MAMUDA) method to solve this problem. Specifically, this method collaboratively learns the feature transformations for different views in different tasks by exploring the shared task-specific and problem intrinsic structures. Additionally, MAMUDA method is convenient to solve the multi-class classification problems. Finally, the experiments on two real-world problems demonstrate the effectiveness of MAMUDA for heterogeneous tasks.

Information diffusion in social networks is emerging as a promising solution to successful viral marketing, which relies on the effective and efficient identification of a set of nodes with the maximal social influence. While there are tremendous efforts on the development of social influence models and algorithms for social influence maximization, limited progress has been made in terms of designing both efficient and effective algorithms for finding a set of nodes with the maximal social influence. To this end, in this paper, we provide a bounded linear approach for influence computation and influence maximization. Specifically, we first adopt a linear and tractable approach to describe the influence propagation. Then, we develop a quantitative metric, named Group-PageRank, to quickly estimate the upper bound of the social influence based on this linear approach. More importantly, we provide two algorithms Linear and Bound, which exploit the linear approach and Group-PageRank for social influence maximization. Finally, extensive experimental results demonstrate that (a) the adopted linear approach has a close relationship with traditional models and Group-PageRank provides a good estimation of social influence; (b) Linear and Bound can quickly find a set of the most influential nodes and both of them are scalable for large-scale social networks.

Ranking residential real estates based on investment values can provide decision making support for home buyers and thus plays an important role in estate marketplace. In this paper, we aim to develop methods for ranking estates based on investment values by mining users' opinions about estates from online user reviews and offline moving behaviors (e.g., Taxi traces, smart card transactions, check-ins). While a variety of features could be extracted from these data, these features are Interco related and redundant. Thus, selecting good features and integrating the feature selection into the fitting of a ranking model are essential. To this end, in this paper, we first strategically mine the fine-grained discrminative features from user reviews and moving behaviors, and then propose a probabilistic sparse pair wise ranking method for estates. Specifically, we first extract the explicit features from online user reviews which express users' opinions about point of interests (POIs) near an estate. We also mine the implicit features from offline moving behaviors from multiple perspectives (e.g., Direction, volume, velocity, heterogeneity, topic, popularity, etc.). Then we learn an estate ranking predictor by combining a pair wise ranking objective and a sparsity regularization in a unified probabilistic framework. And we develop an effective solution for the optimization problem. Finally, we conduct a comprehensive performance evaluation with real world estate related data, and the experimental results demonstrate the competitive performance of both features and the proposed model.

Technology prospecting is a process to evaluate the potential business values of high tech companies from the technology perspective. In this paper, we provide a new view-angle to understand technology prospecting by studying the evolving distributions of technologies in the companies. Specifically, we first exploit topic models to learn technological context in the form of probabilistic distributions of assignees and locations from large-scale patent documents. Then, we develop a matching solution to measure the relationships between patent topics and the description documents of technology terms. In this way, we can obtain the distribution of technologies for each company. In addition, we are able to assess the technology prospecting of a company by a designed indicator, which allows to compare the levels of discrepancies between the emerging technology distributions available as Garner Hype Cycles and the distribution of technologies of the company. Finally, experimental results on real-world patent data show the effectiveness of our approach for technology prospecting.

The emergence of social networks has provided opportunities for both targeted marketing and viral marketing. By concentrating the efforts on a few key customers, targeted marketing could make the promotion of the items (products) much easier and more cost-effective. On the other hand, viral marketing aims at finding a set of individuals (seeds) to maximize the word-of-mouth propagation of an item. However, these two marketing strategies can only exploit some specific characteristics of the social networks, and the problem of how to combine them together to build a better, stronger business is still open. To that end, in this paper, we propose a general approach for integrated marketing. Specifically, to market a given item, we first generate the item-specific candidate users by a recommendation algorithm, and then select the typical users who have the best balanced utility scores and consumption/social entropy. Next, treating typical users as targeted customers, we study the problem of maximizing information awareness in viral marketing with these constrained targets. Along this line, we define it as a constrained coverage maximization problem, and propose three solutions: GMIC, LMIC and QMIC. Finally, extensive experimental results on real-world datasets demonstrate that our integrated marketing approach could outperform the methods that consider only targeted marketing or viral marketing.

Social media has become one of the most popular marketing channels for many companies, which aims at maximizing their influence by various marketing campaigns conducted from their official accounts on social networks. However, most of these marketing accounts merely focus on the contents of their tweets. Less effort has been made on understanding tweeting time, which is a major contributing factor in terms of attracting customers' attention and maximizing the influence of a social marketing campaign. To that end, in this paper, we provide a focused study of temporal retweeting patterns and their influence on social media marketing campaigns. Specifically, we investigate the users' retweeting patterns by modeling their retweeting behaviors as a generative process, which considers temporal, social, and topical factors. Moreover, we validate the predictive power of the model on the dataset collected from Sina Weibo, the most popular micro blog platform in China. By discovering the temporal retweeting patterns, we analyze the temporal popular topics and recommend tweets to users in a time-aware manner. Finally, experimental results show that the proposed algorithm outperforms other baseline methods. This model is applicable for companies to conduct their marketing campaigns at the right time on social media.

Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. In this paper, we focus on the identification and optimization of flawed bus routes to improve utilization efficiency of public transportation services, according to people's real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on mobility patterns, we propose a localized transportation mode choice model, with which we can accurately predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.

The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been traditionally addressed through the textual query. In other words, information retrieval techniques will be exploited based on the textual similarity between queries and textual representation of software artifacts, which is generated by collecting words from comments, identifiers, and descriptions of programs. However, in addition to these semantic information, there are rich information embedded in source codes themselves. These source codes, if analyzed properly, can be a rich source for enhancing the efforts of software artifact retrieval. To this end, in this paper, we develop a feature extraction method on source codes. Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes. Moreover, we design a heterogeneous metric learning approach, which allows to integrate code features and text features into the same latent semantic space. This, in turn, can help to measure the artifact similarity by exploiting the joint power of both code and text features. Finally, extensive experiments on real-world data show that the proposed method can help to improve the performances of software artifact retrieval with a significant margin.

It is traditionally a challenge for home buyers to understand, compare and contrast the investment values of real estates. While a number of estate appraisal methods have been developed to value real property, the performances of these methods have been limited by the traditional data sources for estate appraisal. However, with the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of estates for enhancing estate appraisal. Indeed, the geographic dependencies of the value of an estate can be from the characteristics of its own neighborhood (individual), the values of its nearby estates (peer), and the prosperity of the affiliated latent business area (zone). To this end, in this paper, we propose a geographic method, named ClusRanking, for estate appraisal by leveraging the mutual enforcement of ranking and clustering power. ClusRanking is able to exploit geographic individual, peer, and zone dependencies in a probabilistic ranking model. Specifically, we first extract the geographic utility of estates from geography data, estimate the neighborhood popularity of estates by mining taxicab trajectory data, and model the influence of latent business areas via ClusRanking. Also, we use a linear model to fuse these three influential factors and predict estate investment values. Moreover, we simultaneously consider individual, peer and zone dependencies, and derive an estate-specific ranking likelihood as the objective function. Finally, we conduct a comprehensive evaluation with real-world estate related data, and the experimental results demonstrate the effectiveness of our method.

Advances in real-time location system (RTLS) solutions have enabled us to collect massive amounts of fine-grained semantically rich location traces, which provide unparalleled opportunities for understanding human activities and discovering useful knowledge. This, in turn, delivers intelligence for real-time decision making in various fields, such as workflow management. Indeed, it is a new paradigm for workflow modeling by the knowledge discovery in location traces. To that end, in this paper, we provide a focused study of workflow modeling by the integrated analysis of indoor location traces in the hospital environment. In comparison with conventional workflow modeling based on passive workflow logs, one salient feature of our approach is that it can proactively unravel the workflow patterns hidden in the location traces, by automatically constructing the workflow states and estimating parameters describing the transition patterns of moving objects. Specifically, to determine a meaningful granularity for the model, the workflow states are first constructed as regions associated with specific healthcare activities. Then, we transform the original indoor location traces to the sequences of workflow states and model the workflow transition patterns by finite state machines. Furthermore, we leverage the correlations in the location traces between related types of medical devices to reinforce the modeling performance and enable more applications. The results show that the proposed framework can not only model the workflow patterns effectively, but also have managerial applications in workflow monitoring, auditing, and inspection of workflow compliance, which are critical in the healthcare industry.

Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in a sequence. With the growing complexity of real-world dynamic scenarios, more and more symbols are often needed to encode a meaningful sequence. This is so-called 'curse of cardinality', which can impose significant challenges to the design of sequential analysis methods in terms of computational efficiency and practical use. Indeed, given the overwhelming scale and the heterogeneous nature of the sequential data, new visions and strategies are needed to face the challenges. To this end, in this paper, we propose a 'temporal skeletonization' approach to proactively reduce the representation of sequences to uncover significant, hidden temporal structures. The key idea is to summarize the temporal correlations in an undirected graph. Then, the 'skeleton' of the graph serves as a higher granularity on which hidden temporal patterns are more likely to be identified. In the meantime, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data. Our approach has shown to greatly alleviate the curse of cardinality in challenging tasks of sequential pattern mining and clustering. Evaluation on a Business-to-Business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy customer event data.

The GPS technology and new forms of urban geography have changed the paradigm for mobile services. As such, the abundant availability of GPS traces has enabled new ways of doing taxi business. Indeed, recent efforts have been made on developing mobile recommender systems for taxi drivers using Taxi GPS traces. These systems can recommend a sequence of pick-up points for the purpose of maximizing the probability of identifying a customer with the shortest driving distance. However, in the real world, the income of taxi drivers is strongly correlated with the effective driving hours. In other words, it is more critical for taxi drivers to know the actual driving routes to minimize the driving time before finding a customer. To this end, in this paper, we propose to develop a cost-effective recommender system for taxi drivers. The design goal is to maximize their profits when following the recommended routes for finding passengers. Specifically, we first design a net profit objective function for evaluating the potential profits of the driving routes. Then, we develop a graph representation of road networks by mining the historical taxi GPS traces and provide a Brute-Force strategy to generate optimal driving route for recommendation. However, a critical challenge along this line is the high computational cost of the graph based approach. Therefore, we develop a novel recursion strategy based on the special form of the net profit function for searching optimal candidate routes efficiently. Particularly, instead of recommending a sequence of pick-up points and letting the driver decide how to get to those points, our recommender system is capable of providing an entire driving route, and the drivers are able to find a customer for the largest potential profit by following the recommendations. This makes our recommender system more practical and profitable than other existing recommender systems. Finally, we carry out extensive experiments on a real-world data set collected from the San Francisco Bay area and the experimental results clearly validate the effectiveness of the proposed recommender system.

With the rapid prevalence of smart mobile devices, the number of mobile Apps available has exploded over the past few years. To facilitate the choice of mobile Apps, existing mobile App recommender systems typically recommend popular mobile Apps to mobile users. However, mobile Apps are highly varied and often poorly understood, particularly for their activities and functions related to privacy and security. Therefore, more and more mobile users are reluctant to adopt mobile Apps due to the risk of privacy invasion and other security concerns. To fill this crucial void, in this paper, we propose to develop a mobile App recommender system with privacy and security awareness. The design goal is to equip the recommender system with the functionality which allows to automatically detect and evaluate the security risk of mobile Apps. Then, the recommender system can provide App recommendations by considering both the Apps' popularity and the users' security preferences. Specifically, a mobile App can lead to security risk because insecure data access permissions have been implemented in this App. Therefore, we first develop the techniques to automatically detect the potential security risk for each mobile App by exploiting the requested permissions. Then, we propose a flexible approach based on modern portfolio theory for recommending Apps by striking a balance between the Apps' popularity and the users' security concerns, and build an App hash tree to efficiently recommend Apps. Finally, we evaluate our approach with extensive experiments on a large-scale data set collected from Google Play. The experimental results clearly validate the effectiveness of our approach.

If properly analyzed, the multi-aspect rating data could be a source of rich intelligence for providing personalized restaurant recommendations. Indeed, while recommender systems have been studied for various applications and many recommendation techniques have been developed for general or specific recommendation tasks, there are few studies for restaurant recommendation by addressing the unique challenges of the multi-aspect restaurant reviews. As we know, traditional collaborative filtering methods are typically developed for single aspect ratings. However, multi-aspect ratings are often collected from the restaurant customers. These ratings can reflect multiple aspects of the service quality of the restaurant. Also, geographic factors play an important role in restaurant recommendation. To this end, in this paper, we develop a generative probabilistic model to exploit the multi-aspect ratings of restaurants for restaurant recommendation. Also, the geographic proximity is integrated into the probabilistic model to capture the geographic influence. Moreover, the profile information, which contains customer/restaurant-independent features and the shared features, is also integrated into the model. Finally, we conduct a comprehensive experimental study on a real-world data set. The experimental results clearly demonstrate the benefit of exploiting multi-aspect ratings and the improvement of the developed generative probabilistic model.

Providing better travel services for tourists is one of the important applications in urban computing. Though many recommender systems have been developed for enhancing the quality of travel service, most of them lack a systematic and open framework to dynamically incorporate multiple types of additional context information existing in the tourism domain, such as the travel area, season, and price of travel packages. To that end, in this article, we propose an open framework, the Objected-Oriented Recommender System (ORS), for the developers performing personalized travel package recommendations to tourists. This framework has the ability to import all the available additional context information to the travel package recommendation process in a cost-effective way. Specifically, the different types of additional information are extracted and uniformly represented as feature--value pairs. Then, we define the Object, which is the collection of the feature--value pairs. We propose two models that can be used in the ORS framework for extracting the implicit relationships among Objects. The Objected-Oriented Topic Model (OTM) can extract the topics conditioned on the intrinsic feature--value pairs of the Objects. The Objected-Oriented Bayesian Network (OBN) can effectively infer the cotravel probability of two tourists by calculating the co-occurrence time of feature--value pairs belonging to different kinds of Objects. Based on the relationships mined by OTM or OBN, the recommendation list is generated by the collaborative filtering method. Finally, we evaluate these two models and the ORS framework on real-world travel package data, and the experimental results show that the ORS framework is more flexible in terms of incorporating additional context information, and thus leads to better performances for travel package recommendations. Meanwhile, for feature selection in ORS, we define the feature information entropy, and the experimental results demonstrate that using features with lower entropies usually leads to better recommendation results.

Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.

The problem of mobile context recognition targets the identification of semantic meaning of context in a mobile environment. This plays an important role in understanding mobile user behaviors and thus provides the opportunity for the development of better intelligent context-aware services. A key step of context recognition is to model the personalized contextual information of mobile users. Although many studies have been devoted to mobile context modeling, limited efforts have been made on the exploitation of the sequential and dependency characteristics of mobile contextual information. Also, the latent semantics behind mobile context are often ambiguous and poorly understood. Indeed, a promising direction is to incorporate some domain knowledge of common contexts, such as “waiting for a bus” or “having dinner,” by modeling both labeled and unlabeled context data from mobile users because there are often few labeled contexts available in practice. To this end, in this article, we propose a sequence-based semisupervised approach to modeling personalized context for mobile users. Specifically, we first exploit the Bayesian Hidden Markov Model (B-HMM) for modeling context in the form of probabilistic distributions and transitions of raw context data. Also, we propose a sequential model by extending B-HMM with the prior knowledge of contextual features to model context more accurately. Then, to efficiently learn the parameters and initial values of the proposed models, we develop a novel approach for parameter estimation by integrating the Dirichlet Process Mixture (DPM) model and the Mixture Unigram (MU) model. Furthermore, by incorporating both user-labeled and unlabeled data, we propose a semisupervised learning-based algorithm to identify and model the latent semantics of context. Finally, experimental results on real-world data clearly validate both the efficiency and effectiveness of the proposed approaches for recognizing personalized context of mobile users.

Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data. Experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist relationships, the TRAST model can be used as an effective assessment for travel group formation.

Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data could be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendations. However, tour recommendation is quite different from traditional recommendations, because the tourist’s choice is affected directly by the travel costs, which includes both financial and time costs. To that end, in this article, we provide a focused study of cost-aware tour recommendation. Along this line, we first propose two ways to represent user cost preference. One way is to represent user cost preference by a two-dimensional vector. Another way is to consider the uncertainty about the cost that a user can afford and introduce a Gaussian prior to model user cost preference. With these two ways of representing user cost preference, we develop different cost-aware latent factor models by incorporating the cost information into the probabilistic matrix factorization (PMF) model, the logistic probabilistic matrix factorization (LPMF) model, and the maximum margin matrix factorization (MMMF) model, respectively. When applied to real-world travel tour data, all the cost-aware recommendation models consistently outperform existing latent factor models with a significant margin.

While a massive amount of time series can now be collected in many physical systems, it is a challenge to build an analytic model that can correctly profile the data because those time series usually exhibit various behaviors. In this paper we propose an integrated method to address the heterogeneity issue in modeling big time series data. We first extracts relevant features to summarize the underlying dynamics of those series. We present both linear and nonlinear feature extraction techniques, as well as a procedure to determine the right extraction method for individual time series. Given extracted features, our method further models the trajectory pattern of time series in the feature space. Both a regression based and a density based method are presented to profile different types of feature trajectories. Experimental results in a real power plant illustrate that our feature extraction and trajectory model are effective to profile various time series. Our method has been used to successfully detect anomalies in the system.

Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App develops to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we investigate two types of evidences, ranking based evidences and rating based evidences, by modeling Apps' ranking and rating behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the Apple's App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

The problem of point of interest (POI) recommendation is to provide personalized recommendations of places of interests, such as restaurants, for mobile users. Due to its complexity and its connection to location based social networks (LBSNs), the decision process of a user choose a POI is complex and can be influenced by various factors, such as user preferences, geographical influences, and user mobility behaviors. While there are some studies on POI recommendations, it lacks of integrated analysis of the joint effect of multiple factors. To this end, in this paper, we propose a novel geographical probabilistic factor analysis framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user's check-in behavior. Also, the user mobility behaviors can be effectively exploited in the recommendation model. Moreover, the recommendation model can effectively make use of user check-in count data as implicity user feedback for modeling user preferences. Finally, experimental results on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art latent factor models with a significant margin.

The Cloud-based infrastructure-as-a-service (IaaS) paradigm (e.g., Amazon EC2) enables a client who lacks computational resources to outsource her dataset and data mining tasks to the Cloud. However, as the Cloud may not be fully trusted, it raises serious concerns about the integrity of the mining results returned by the Cloud. To this end, in this paper, we provide a focused study about how to perform integrity verification of the k-means clustering task outsourced to an IaaS provider. We consider the untrusted sloppy IaaS service provider that intends to return wrong clustering results by terminating the iterations early to save computational cost. We develop both probabilistic and deterministic verification methods to catch the incorrect clustering result by the service provider. The deterministic method returns 100% integrity guarantee with cost that is much cheaper than executing k-means clustering locally, while the probabilistic method returns a probabilistic integrity guarantee with computational cost even cheaper than the deterministic approach. Our experimental results show that our verification methods can effectively and efficiently capture the sloppy service provider.

The wide spread use of location based social networks (LBSNs) has enabled the opportunities for better location based services through Point-of-Interest (POI) recommendation. Indeed, the problem of POI recommendation is to provide personalized recommendations of places of interest. Unlike traditional recommendation tasks, POI recommendation is personalized, location-aware, and context depended. In light of this difference, this paper proposes a topic and location aware POI recommender system by exploiting associated textual and context information. Specifically, we first exploit an aggregated latent Dirichlet allocation (LDA) model to learn the interest topics of users and to infer the interest POIs by mining textual information associated with POIs. Then, a Topic and Location-aware probabilistic matrix factorization (TL-PMF) method is proposed for POI recommendation. A unique perspective of TL-PMF is to consider both the extent to which a user interest matches the POI in terms of topic distribution and the word-of-mouth opinions of the POIs. Finally, experiments on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art probabilistic latent factor models with a significant margin. Also, we have studied the impact of personalized interest topics and word-of-mouth opinions on POI recommendations.

No abstract available.

The management of large-scale distributed information systems relies on the effective use and modeling of monitoring data collected at various points in the distributed information systems. A traditional approach to model monitoring data is to discover invariant relationships among the monitoring data. Indeed, we can discover all invariant relationships among all pairs of monitoring data and generate invariant networks, where a node is a monitoring data source (metric) and a link indicates an invariant relationship between two monitoring data. Such an invariant network representation can help system experts to localize and diagnose the system faults by examining those broken invariant relationships and their related metrics, since system faults usually propagate among the monitoring data and eventually lead to some broken invariant relationships. However, at one time, there are usually a lot of broken links (invariant relationships) within an invariant network. Without proper guidance, it is difficult for system experts to manually inspect this large number of broken links. To this end, in this article, we propose the problem of ranking metrics according to the anomaly levels for a given invariant network, while this is a nontrivial task due to the uncertainties and the complex nature of invariant networks. Specifically, we propose two types of algorithms for ranking metric anomaly by link analysis in invariant networks. Along this line, we first define two measurements to quantify the anomaly level of each metric, and introduce the m R ank algorithm. Also, we provide a weighted score mechanism and develop the g R ank algorithm, which involves an iterative process to obtain a score to measure the anomaly levels. In addition, some extended algorithms based on m R ank and g R ank algorithms are developed by taking into account the probability of being broken as well as noisy links. Finally, we validate all the proposed algorithms on a large number of real-world and synthetic data sets to illustrate the effectiveness and efficiency of different algorithms.

In this paper, we introduce and study the minimum consistent subset cover (MCSC) problem. Given a finite ground set X and a constraint t, find the minimum number of consistent subsets that cover X, where a subset of X is consistent if it satisfies t. The MCSC problem generalizes the traditional set covering problem and has minimum clique partition (MCP), a dual problem of graph coloring, as an instance. Many common data mining tasks in rule learning, clustering, and pattern mining can be formulated as MCSC instances. In particular, we discuss the minimum rule set (MRS) problem that minimizes model complexity of decision rules, the converse k-clustering problem that minimizes the number of clusters, and the pattern summarization problem that minimizes the number of patterns. For any of these MCSC instances, our proposed generic algorithm CAG can be directly applicable. CAG starts by constructing a maximal optimal partial solution, then performs an example-driven specific-to-general search on a dynamically maintained bipartite assignment graph to simultaneously learn a set of consistent subsets with small cardinality covering the ground set.

A key step for the mobile app usage analysis is to classify apps into some predefined categories. However, it is a nontrivial task to effectively classify mobile apps due to the limited contextual information available for the analysis. To this end, in this paper, we propose an approach to first enrich the contextual information of mobile apps by exploiting the additional Web knowledge from the Web search engine. Then, inspired by the observation that different types of mobile apps may be relevant to different real-world contexts, we also extract some contextual features for mobile apps from the context-rich device logs of mobile users. Finally, we combine all the enriched contextual information into a Maximum Entropy model for training a mobile app classifier. The experimental results based on 443 mobile users' device logs clearly show that our approach outperforms two state-of-the-art benchmark methods with a significant margin.

Rapid growth in the development of real-time location system solutions has led to an increased interest in indoor location-aware services, such as hospital asset management. Although there are extensive studies in the literature on the analysis of outdoor location traces, the studies of indoor location traces are less touched and fragmented. To that end, in this paper, we provide a focused study of indoor location traces collected by the sensors attached to medical devices in a hospital environment. Along this line, we first introduce some unique properties of these indoor location traces. We show that they can capture the movement patterns of the medical devices, which are tightly coupled with the work flow in the controlled hospital environment. Based on this observation, we propose a stochastic model for context-aware anomaly detection in indoor location traces, which exploits the hospital work flow and models the movements of medical devices as transitions in finite state machines. In detail, we first develop a density-based method to identify the hotspots filled with high-level abnormal activities in the indoor environment. The discovered hotspots serve as the context for nearby trajectories. Then, we introduce an N-gram based method for measuring the degree of anomaly based on the detected hotspots, which is able to predict the missing events possibly due to the devices being stolen. Besides, to address the noisy nature of the indoor sensor networks, we also propose an iterative algorithm to estimate the transition probabilities. This algorithm allows to effectively recover the missing location records which are critical for the abnormality estimation. Finally, the experimental results on the real-world date sets validate the effectiveness of the proposed context-aware anomaly detection method for identifying abnormal events.

In this paper, we illustrate how to extract personal context-aware preferences from the context-rich device logs (i.e., context logs) for building novel personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his/her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context independent and context dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world data set show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.

User habit mining plays an important role in user understanding, which is critical for improving a wide range of personalized intelligence services. Recently, some researchers proposed to mine user behavior patterns which characterize the habits of mobile users and account for the associations between user interactions and context captured by mobile devices. However, the existing approaches for mining these behavior patterns are not practical in mobile environments due to limited computing resources on mobile devices. To fulfill this crucial void, we investigate optimizing strategies which can be used for improving the efficiency of behavior pattern mining in terms of computing and memory needs. Specifically, we examine typical optimizing strategies for association rule mining and study the feasibility of applying them to behavior pattern mining, since these two problems are similar in many aspects. Moreover, we develop an efficient algorithm, named BP-Growth, for behavior pattern mining by combining two promising strategies. Finally, experimental results show that BP-Growth outperforms benchmark methods with a significant margin in terms of both computing and memory cost.

In this paper, we present a systematic perspective study on choosing and evaluating the initial seed items that will be recommended to the cold start users. We first construct an item consumption correlation network to capture the existing users' general consumption behaviors. Then, we formalize initial items recommendation as the influential seed set selection problem. Along this line, we present several methods, each of which selects seed items according to different rules. Finally, the experimental results on two real-world data sets verify that with different seed items, the users' consumption numbers will be quite different. Meanwhile, the results also provide many deep insights into these selection methods and their recommended seed items.

It is well known that the distance metric plays an important role in the clustering process. Indeed, many clustering problems can be treated as an optimization problem of a criterion function defined over one distance metric. While many distance metrics have been developed, it is not clear that how these distance metrics can impact on the clustering/optimization process. To that end, in this paper, we study the impact of a set of popular cosine-based distance metrics on K-means clustering. Specifically, by revealing the common order-preserving property, we first show that K-means has exactly the same cluster assignment for these metrics during the E-step. Next, by both theoretical and empirical studies, we prove that the cluster centroid is a good approximator of their respective optimal centers in the M-step. As such, we identify a problem with K-means: it cannot differentiate these metrics. To explore the nature of these metrics, we propose an evolutionary K-means framework that integrates K-means and genetic algorithms. This framework not only enables inspection of arbitrary distance metrics, but also can be used to investigate different formulations of the optimization problem. Finally, this framework is used in extensive experiments on real-world data sets. The results validate our theoretical findings on the characteristics and interrelationships of these metrics. Most importantly, this paper furthers our understanding of the impact of the distance metrics on the optimization process of K-means.

Order-preserving submatrix (OPSM) has been widely accepted as a biologically meaningful cluster model, capturing the general tendency of gene expression across a subset of experiments. In an OPSM, the expression levels of all genes induce the same linear ordering of the experiments. The OPSM problem is to discover those statistically significant OPSMs from a given data matrix. The problem is reducible to a special case of the sequential pattern mining problem, where a pattern and its supporting sequences uniquely specify an OPSM. Unfortunately, existing methods do not scale well to massive data sets containing thousands of experiments and hundreds of thousands of genes, which are common in today's gene expression analysis. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs in their discovery and are completely pruned off by existing methods. However, it is of particular interest of biologists to determine small groups of genes that are tightly coregulated across many experiments, and some pathways or processes may require as few as two genes to act in concert. In this paper, we study the discovery of deep OPSMs from massive data sets. We propose a novel best effort mining framework Kiwi that exploits two parameters k and w to bound the available computational resources and search a selected search space, and does what it can to find as many as possible deep OPSMs. Extensive biological and computational evaluations on real data sets demonstrate the validity and importance of the deep OPSM problem, and the efficiency and effectiveness of the Kiwi mining framework.

The distribution difference among multiple domains has been exploited for cross-domain text categorization in recent years. Along this line, we show two new observations in this study. First, the data distribution difference is often due to the fact that different domains use different index words to express the same concept. Second, the association between the conceptual feature and the document class can be stable across domains. These two observations actually indicate the distinction and commonality across domains. Inspired by the above observations, we propose a generative statistical model, named Collaborative Dual-PLSA (CD-PLSA), to simultaneously capture both the domain distinction and commonality among multiple domains. Different from Probabilistic Latent Semantic Analysis (PLSA) with only one latent variable, the proposed model has two latent factors y and z, corresponding to word concept and document class, respectively. The shared commonality intertwines with the distinctions over multiple domains, and is also used as the bridge for knowledge transformation. An Expectation Maximization (EM) algorithm is developed to solve the CD-PLSA model, and further its distributed version is exploited to avoid uploading all the raw data to a centralized location and help to mitigate privacy concerns. After the training phase with all the data from multiple domains we propose to refine the immediate outputs using only the corresponding local data. In summary, we propose a two-phase method for cross-domain text classification, the first phase for collaborative training with all the data, and the second step for local refinement. Finally, we conduct extensive experiments over hundreds of classification tasks with multiple source domains and multiple target domains to validate the superiority of the proposed method over existing state-of-the-art methods of supervised and transfer learning. It is noted to mention that as shown by the experimental results CD-PLSA for the collaborative training is more tolerant of distribution differences, and the local refinement also gains significant improvement in terms of classification accuracy.

The distribution difference among multiple domains has been exploited for cross-domain text categorization in recent years. Along this line, we show two new observations in this study. First, the data distribution difference is often due to the fact that different domains use different index words to express the same concept. Second, the association between the conceptual feature and the document class can be stable across domains. These two observations actually indicate the distinction and commonality across domains. Inspired by the above observations, we propose a generative statistical model, named Collaborative Dual-PLSA (CD-PLSA), to simultaneously capture both the domain distinction and commonality among multiple domains. Different from Probabilistic Latent Semantic Analysis (PLSA) with only one latent variable, the proposed model has two latent factors y and z, corresponding to word concept and document class, respectively. The shared commonality intertwines with the distinctions over multiple domains, and is also used as the bridge for knowledge transformation. An Expectation Maximization (EM) algorithm is developed to solve the CD-PLSA model, and further its distributed version is exploited to avoid uploading all the raw data to a centralized location and help to mitigate privacy concerns. After the training phase with all the data from multiple domains we propose to refine the immediate outputs using only the corresponding local data. In summary, we propose a two-phase method for cross-domain text classification, the first phase for collaborative training with all the data, and the second step for local refinement. Finally, we conduct extensive experiments over hundreds of classification tasks with multiple source domains and multiple target domains to validate the superiority of the proposed method over existing state-of-the-art methods of supervised and transfer learning. It is noted to mention that as shown by the experimental results CD-PLSA for the collaborative training is more tolerant of distribution differences, and the local refinement also gains significant improvement in terms of classification accuracy.

Recent years have witnessed increased efforts on detecting topics and events from Web search logs, since this kind of data not only capture web content but also reflect the users’ activities. However, the majority of existing work is focused on exploiting clustering techniques for topic and event detection. Due to the huge size and the evolving nature of Web data, existing clustering approaches are limited to meet the real-time demand. To that end, in this article, we propose a method called LETD to detect evolving topics in a timely manner. Also, we design the techniques to extract events from topics and to infer the evolving relationship among the events. For topic detection, we first provide a measurement to select the important URLs, which are most likely to describe a real-life topic. Then, starting from these selected URLs, we exploit the local expansion method to find other topic-related URLs. Moreover, in the LETD framework, we design algorithms based on Random Walk and Markov Random Fields (MRF), respectively. Because the LETD method exploits a divide-and-conquer strategy to process the data, it is more efficient than existing methods based on clustering techniques. To better illustrate the LETD framework, we develop a demo system StoryTeller which can discover hot topics and events, infer the evolving relationships among events, and visualize information in a storytelling way. This demo system can provide a global view of the topic development and help users target the interesting events more conveniently. Finally, experimental results on real-world Microsoft click-through data have shown that StoryTeller can find real-life hot topics and meaningful evolving relationships among events, and has also demonstrated the efficiency and effectiveness of the LETD method.

How to improve authority ranking is a crucial research problem for expert finding. In this paper, we propose a novel framework for expert finding based on the authority information in the target category as well as the relevant categories. First, we develop a scalable method for measuring the relevancy between categories through topic models. Then, we provide a link analysis approach for ranking user authority by considering the information in both the target category and the relevant categories. Finally, the extensive experiments on two large-scale real-world Q&A data sets clearly show that the proposed method outperforms the baseline methods with a significant margin.

Advances in GPS tracking technology have enabled us to install GPS tracking devices in city taxis to collect a large amount of GPS traces under operational time constraints. These GPS traces provide unparallel opportunities for us to uncover taxi driving fraud activities. In this paper, we develop a taxi driving fraud detection system, which is able to systematically investigate taxi driving fraud. In this system, we first provide functions to find two aspects of evidences: travel route evidence and driving distance evidence. Furthermore, a third function is designed to combine the two aspects of evidences based on Dempster-Shafer theory. To implement the system, we first identify interesting sites from a large amount of taxi GPS logs. Then, we propose a parameter-free method to mine the travel route evidences. Also, we introduce route mark to represent a typical driving path from an interesting site to another one. Based on route mark, we exploit a generative statistical model to characterize the distribution of driving distance and identify the driving distance evidences. Finally, we evaluate the taxi driving fraud detection system with large scale real-world taxi GPS logs. In the experiments, we uncover some regularity of driving fraud activities and investigate the motivation of drivers to commit a driving fraud by analyzing the produced taxi fraud data.

As the worlds of commerce, entertainment, travel, and Internet technology become more inextricably linked, new types of business data become available for creative use and formal analysis. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To this end, we first analyze the characteristics of the travel packages and develop a Tourist-Area-Season Topic (TAST) model, which can extract the topics conditioned on both the tourists and the intrinsic features (i.e. locations, travel seasons) of the landscapes. Based on this TAST model, we propose a cocktail approach on personalized travel package recommendation. Finally, we evaluate the TAST model and the cocktail approach on real-world travel package data. The experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is thus much more effective than traditional recommendation methods for travel package recommendation.

The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract useful business intelligence, which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors, improving customer experiences, and increasing business performances. However, extracting business intelligence from location traces is not a trivial task. Conventional data analytic tools are usually not customized for handling large, complex, dynamic, and distributed nature of location traces. To that end, we develop a taxi business intelligence system to explore the massive taxi location traces from different business perspectives with various data mining functions. Since we implement the system using the real-world taxi GPS data, this demonstration will help taxi companies to improve their business performances by understanding the behaviors of both drivers and customers. In addition, several identified technical challenges also motivate data mining people to develop more sophisticate techniques in the future.

Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data can be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendations. However, tour recommendation is quite different from traditional recommendations, because the tourist's choice is directly affected by the travel cost, which includes the financial cost and the time. To that end, in this paper, we provide a focused study of cost-aware tour recommendation. Along this line, we develop two cost-aware latent factor models to recommend travel packages by considering both the travel cost and the tourist's interests. Specifically, we first design a cPMF model, which models the tourist's cost with a 2-dimensional vector. Also, in this cPMF model, the tourist's interests and the travel cost are learnt by exploring travel tour data. Furthermore, in order to model the uncertainty in the travel cost, we further introduce a Gaussian prior into the cPMF model and develop the GcPMF model, where the Gaussian prior is used to express the uncertainty of the travel cost. Finally, experiments on real-world travel tour data show that the cost-aware recommendation models outperform state-of-the-art latent factor models with a significant margin. Also, the GcPMF model with the Gaussian prior can better capture the impact of the uncertainty of the travel cost, and thus performs better than the cPMF model.

P2P lending, as a novel economic lending model, has imposed new challenges about how to make effective investment decisions. Indeed, a key challenge along this line is how to align the right information with the right people. For a long time, people have made tremendous efforts in establishing credit records for the borrowers. However, information from investors is still under-explored for improving investment decisions in P2P lending. To that end, we propose a data driven investment decision-making framework, which exploits the investor composition of each investment for enhancing decisions making in P2P lending. Specifically, we first build investor profiles based on quantitative analysis of past performances, risk preferences, and investment experiences of investors. Then, based on investor profiles, we develop an investor composition analysis model, which can be used to select valuable investments and improve the investment decisions. To validate the proposed model, we perform extensive experiments on the real-world data from the world's largest P2P lending marketplace. Experimental results reveal that investor composition can help us evaluate the profit potential of an investment and the decision model based on investor composition can help investors make better investment decisions.

Rating sparsity is a critical issue for collaborative filtering. For example, the well-known Netflix Movie rating data contain ratings of only about 1% user-item pairs. One way to address this rating sparsity problem is to develop more effective methods for training rating prediction models. To this end, in this paper, we introduce a collective training paradigm to automatically and effectively augment the training ratings. Essentially, the collective training paradigm builds multiple different Collaborative Filtering (CF) models separately, and augments the training ratings of each CF model by using the partial predictions of other CF models for unknown ratings. Along this line, we develop two algorithms, Bi-CF and Tri-CF, based on collective training. For Bi-CF and Tri-CF, we collectively and iteratively train two and three different CF models via iteratively augmenting training ratings for individual CF model. We also design different criteria to guide the selection of augmented training ratings for Bi-CF and Tri-CF. Finally, the experimental results show that Bi-CF and Tri-CF algorithms can significantly outperform baseline methods, such as neighborhood-based and SVD-based models.

In this study, we formalize a multifocal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within each focal group. The multifocal learning problem is motivated by numerous real-world learning applications. For instance, for the same type of problems encountered in a customer service center, the problem descriptions from different customers can be quite different. Experienced customers usually give more precise and focused descriptions about the problem. In contrast, inexperienced customers usually provide diverse descriptions. In this case, the examples from the same class in the training data can be naturally in different focal groups. Therefore, it is necessary to identify those natural focal groups and exploit them for learning at different focuses. Along this line, the key development challenge is how to identify those focal groups in the training data. As a case study, we exploit multifocal learning for profiling customer problems. Also, we provide an empirical study about how the performance of multifocal learning is affected by the quality of focal groups. The results on real-world customer problem logs show that multifocal learning can significantly boost the performance of many existing classification algorithms, such as Support Vector Machines (SVMs), for classifying customer problems and there is strong correlation between the quality of focal groups and the learning performance.

The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for identifying abnormal moving activities. Indeed, various aspects of abnormality of moving patterns have recently been exploited, such as wrong direction and wandering. However, there is no recognized way of combining different aspects into an unified evolving abnormality score which has the ability to capture the evolving nature of abnormal moving trajectories. To that end, in this paper, we provide an evolving trajectory outlier detection method, named TOP-EYE, which continuously computes the outlying score for each trajectory in an accumulating way. Specifically, in TOP-EYE, we introduce a decay function to mitigate the influence of the past trajectories on the evolving outlying score, which is defined based on the evolving moving direction and density of trajectories. This decay function enables the evolving computation of accumulated outlying scores along the trajectories. An advantage of TOP-EYE is to identify evolving outliers at very early stage with relatively low false alarm rate. Finally, experimental results on real-world location traces show that TOP-EYE can effectively capture evolving abnormal trajectories.

In real applications, a given user buys or rates an item based on his/her interests. Learning to leverage this interest information is often critical for recommender systems. However, in existing recommender systems, the information about latent user interests are largely under-explored. To that end, in this paper, we propose an interest expansion strategy via personalized ranking based on the topic model, named iExpand, for building an interest-oriented collaborative filtering framework. The iExpand method introduces a three-layer, user-interest-item, representation scheme, which leads to more interpretable recommendation results and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues, such as the overspecialization and the cold-start problems. Finally, we evaluate iExpand on benchmark data sets, and experimental results show that iExpand outperforms state-of-the-art methods.

The distribution difference among multiple data domains has been considered for the cross-domain text classification problem. In this study, we show two new observations along this line. First, the data distribution difference may come from the fact that different domains use different key words to express the same concept. Second, the association between this conceptual feature and the document class may be stable across domains. These two issues are actually the distinction and commonality across data domains. Inspired by the above observations, we propose a generative statistical model, named Collaborative Dual-PLSA (CD-PLSA), to simultaneously capture both the domain distinction and commonality among multiple domains. Different from Probabilistic Latent Semantic Analysis (PLSA) with only one latent variable, the proposed model has two latent factors y and z, corresponding to word concept and document class respectively. The shared commonality intertwines with the distinctions over multiple domains, and is also used as the bridge for knowledge transformation. We exploit an Expectation Maximization (EM) algorithm to learn this model, and also propose its distributed version to handle the situation where the data domains are geographically separated from each other. Finally, we conduct extensive experiments over hundreds of classification tasks with multiple source domains and multiple target domains to validate the superiority of the proposed CD-PLSA model over existing state-of-the-art methods of supervised and transfer learning. In particular, we show that CD-PLSA is more tolerant of distribution differences.

Mobile context modeling is a process of recognizing and reasoning about contexts and situations in a mobile environment, which is critical for the success of context-aware mobile services. While there are prior work on mobile context modeling, the use of unsupervised learning techniques for mobile context modeling is still under-explored. Indeed, unsupervised techniques have the ability to learn personalized contexts which are difficult to be predefined. To that end, in this paper, we propose an unsupervised approach to modeling personalized contexts of mobile users. Along this line, we first segment the raw context data sequences of mobile users into context sessions where a context session contains a group of adjacent context records which are mutually similar and usually reflect the similar contexts. Then, we exploit topic models to learn personalized contexts in the form of probabilistic distributions of raw context data from the context sessions. Finally, experimental results on real-world data show that the proposed approach is efficient and effective for mining personalized contexts of mobile users.

In this paper, we formulate a novel problem for finding black hole and volcano patterns in a large directed graph. Specifically, a black hole pattern is a group which is made of a set of nodes in a way such that there are only in links to this group from the rest nodes in the graph. In contrast, a volcano pattern is a group which only has out links to the rest nodes in the graph. Both patterns can be observed in real world. For instance, in a trading network, a black hole pattern may represent a group of traders who are manipulating the market. In the paper, we first prove that the black hole mining problem is a dual problem of finding volcanoes. Therefore, we focus on finding the black hole patterns. Along this line, we design two pruning schemes to guide the black hole finding process. In the first pruning scheme, we strategically prune the search space based on a set of pattern-size-independent pruning rules and develop an iBlack hole algorithm. The second pruning scheme follows a divide-and-conquer strategy to further exploit the pruning results from the first pruning scheme. Indeed, a target directed graphs can be divided into several disconnected sub graphs by the first pruning scheme, and thus the black hole finding can be conducted in each disconnected sub graph rather than in a large graph. Based on these two pruning schemes, we also develop an iBlackhole-DC algorithm. Finally, experimental results on real-world data show that the iBlackhole-DC algorithm can be several orders of magnitude faster than the iBlackhole algorithm, which has a huge computational advantage over a brute-force method.

Clustering validation has long been recognized as one of the vital issues essential to the success of clustering applications. In general, clustering validation can be categorized into two classes, external clustering validation and internal clustering validation. In this paper, we focus on internal clustering validation and present a detailed study of 11 widely used internal clustering validation measures for crisp clustering. From five conventional aspects of clustering, we investigate their validation properties. Experiment results show that S_Dbw is the only internal validation measure which performs well in all five aspects, while other measures have certain limitations in different application scenarios.

The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract energy-efficient transportation patterns (green knowledge), which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors. However, extracting green knowledge from location traces is not a trivial task. Conventional data analysis tools are usually not customized for handling the massive quantity, complex, dynamic, and distributed nature of location traces. To that end, in this paper, we provide a focused study of extracting energy-efficient transportation patterns from location traces. Specifically, we have the initial focus on a sequence of mobile recommendations. As a case study, we develop a mobile recommender system which has the ability in recommending a sequence of pick-up points for taxi drivers or a sequence of potential parking positions. The goal of this mobile recommendation system is to maximize the probability of business success. Along this line, we provide a Potential Travel Distance (PTD) function for evaluating each candidate sequence. This PTD function possesses a monotone property which can be used to effectively prune the search space. Based on this PTD function, we develop two algorithms, LCP and SkyRoute, for finding the recommended routes. Finally, experimental results show that the proposed system can provide effective mobile sequential recommendation and the knowledge extracted from location traces can be used for coaching drivers and leading to the efficient use of energy.

Cross-domain text categorization targets on adapting the knowledge learnt from a labeled source-domain to an unlabeled target-domain, where the documents from the source and target domains are drawn from different distributions. However, in spite of the different distributions in raw word features, the associations between word clusters (conceptual features) and document classes may remain stable across different domains. In this paper, we exploit these unchanged associations as the bridge of knowledge transformation from the source domain to the target domain by the nonnegative matrix tri-factorization. Specifically, we formulate a joint optimization framework of the two matrix tri-factorizations for the source and target domain data respectively, in which the associations between word clusters and document classes are shared between them. Then, we give an iterative algorithm for this optimization and theoretically show its convergence. The comprehensive experiments show the effectiveness of this method. In particular, we show that the proposed method can deal with some difficult scenarios where baseline methods usually do not perform well.

Classification across different domains studies how to adapt a learning model from one domain to another domain which shares similar data characteristics. While there are a number of existing works along this line, many of them are only focused on learning from a single source domain to a target domain. In particular, a remaining challenge is how to apply the knowledge learned from multiple source domains to a target domain. Indeed, data from multiple source domains can be semantically related, but have different data distributions. It is not clear how to exploit the distribution differences among multiple source domains to boost the learning performance in a target domain. To that end, in this paper, we propose a consensus regularization framework for learning from multiple source domains to a target domain. In this framework, a local classifier is trained by considering both local data available in one source domain and the prediction consensus with the classifiers learned from other source domains. Moreover, we provide a theoretical analysis as well as an empirical study of the proposed consensus regularization framework. The experimental results on text categorization and image classification problems show the effectiveness of this consensus regularization learning method. Finally, to deal with the situation that the multiple source domains are geographically distributed, we also develop the distributed version of the proposed algorithm, which avoids the need to upload all the data to a centralized location and helps to mitigate privacy concerns.

This paper presents a systematic study of how to enhance recommender systems under volatile user interest drifts. A key development challenge along this line is how to track user interests dynamically. To this end, we first define four types of interest patterns to understand users' rating behaviors and analyze the properties of these patterns. We also propose a rating graph and rating chain based approach for detecting these interest patterns. For each users' rating series, a rating graph and a rating chain are constructed based on the similarities between rated items. The type of a given user's interest pattern is identified through the density of the corresponding rating graph and the continuity of the corresponding rating chain. In addition, we propose a general algorithm framework for improving recommender systems by exploiting these identified patterns. Finally, experimental results on a real-world data set show that the proposed rating graph based approach is effective for detecting user interest patterns, which in turn help to improve the performance of recommender systems.

Over the past several years, there has been a great interest in topic detection and tracking (TDT). Recently, analyzing general research trend from the huge amount of history documents also arouses considerable attention. However, existing work on TDT mainly focuses on overall trend analysis, and is unable to address questions such as "what determines the evolution of a topic?" and "when and how does a new topic get formed?".

This paper presents a study of Multi-type Nearest Neighbor (MTNN) Queries in road networks with time window constraints. Specifically, we provide a label correcting algorithm, which is based on a time aggregated multi-type graph. This algorithm gives the best start time, a turn-by-turn route, and the shortest path in terms of least travel time for a given MTNN query.

Given a large transaction database, association analysis is concerned with efficiently finding strongly related objects. Unlike traditional associate analysis, where relationships among variables are searched at a global level, we examine confounding factors at a local level. Indeed, many real-world phenomena are localized to specific regions and times. These relationships may not be visible when the entire data set is analyzed. Specially, confounding effects that change the direction of correlation is the most significant. Along this line, we propose to efficiently find confounding effects attributable to local associations. Specifically, we derive an upper bound by a necessary condition of confounders, which can help us prune the search space and efficiently identify confounders. Experimental results show that the proposed CONFOUND algorithm can effectively identify confounders and the computational performance is an order of magnitude faster than benchmark methods.

In this study, we formalize a multi-focal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within each focal group. The multi-focal learning problem is motivated by numerous real-world learning applications. For instance, for the same type of problems encountered in a customer service center, the problem descriptions from different customers can be quite different. The experienced customers usually give more precise and focused descriptions about the problem. In contrast, the inexperienced customers usually provide more diverse descriptions. In this case, the examples from the same class in the training data can be naturally in different focal groups. As a result, it is necessary to identify those natural focal groups and exploit them for learning at different focuses. The key developmental challenge is how to identify those focal groups in the training data. As a case study, we exploit multi-focal learning for profiling problems in customer service centers. The results show that multifocal learning can significantly boost the learning accuracies of existing learning algorithms, such as Support Vector Machines (SVMs), for classifying customer problems.

Clustering validation is a long standing challenge in the clustering literature. While many validation measures have been developed for evaluating the performance of clustering algorithms, these measures often provide inconsistent information about the clustering performance and the best suitable measures to use in practice remain unknown. This paper thus fills this crucial void by giving an organized study of 16 external validation measures for K-means clustering. Specifically, we first introduce the importance of measure normalization in the evaluation of the clustering performance on data with imbalanced class distributions. We also provide normalization solutions for several measures. In addition, we summarize the major properties of these external measures. These properties can serve as the guidance for the selection of validation measures in different application scenarios. Finally, we reveal the interrelationships among these external measures. By mathematical transformation, we show that some validation measures are equivalent. Also, some measures have consistent validation performances. Most importantly, we provide a guide line to select the most suitable validation measures for K-means clustering.

This paper studies the generalization and normalization issues of information-theoretic distance measures for clustering validation. Along this line, we first introduce a uniform representation of distance measures, defined as quasi-distance, which is induced based on a general form of conditional entropy. The quasi-distance possesses three properties: symmetry, the triangle law, and the minimum reachable. These properties ensure that the quasi-distance naturally lends itself as the external measure for clustering validation. In addition, we observe that the ranges of the distance measures are different when they apply for clustering validation on different data sets. Therefore, when comparing the performances of clustering algorithms on different data sets, distance normalization is required to equalize ranges of the distance measures. A critical challenge for distance normalization is to obtain the ranges of a distance measure when a data set is provided. To that end, we theoretically analyze the computation of the maximum value of a distance measure for a data set. Finally, we compare the performances of the partition clustering algorithm K-means on various real-world data sets. The experiments show that the normalized distance measures have better performance than the original distance measures when comparing clusterings of different data sets. Also, the normalized Shannon distance has the best performance among four distance measures under study.

Recent years have witnessed an increased interest in transfer learning. Despite the vast amount of research performed in this field, there are remaining challenges in applying the knowledge learnt from multiple source domains to a target domain. First, data from multiple source domains can be semantically related, but have different distributions. It is not clear how to exploit the distribution differences among multiple source domains to boost the learning performance in a target domain. Second, many real-world applications demand this transfer learning to be performed in a distributed manner. To meet these challenges, we propose a consensus regularization framework for transfer learning from multiple source domains to a target domain. In this framework, a local classifier is trained by considering both local data available in a source domain and the prediction consensus with the classifiers from other source domains. In addition, the training algorithm can be implemented in a distributed manner, in which all the source-domains are treated as slave nodes and the target domain is used as the master node. To combine the training results from multiple source domains, it only needs share some statistical data rather than the full contents of their labeled data. This can modestly relieve the privacy concerns and avoid the need to upload all data to a central location. Finally, our experimental results show the effectiveness of our consensus regularization learning.

Information-theoretic clustering aims to exploit information-theoretic measures as the clustering criteria. A common practice on this topic is the so-called Info-Kmeans, which performs K-means clustering with KL-divergence as the proximity function. While expert efforts on Info-Kmeans have shown promising results, a remaining challenge is to deal with high-dimensional sparse data such as text corpora. Indeed, it is possible that the centroids contain many zero-value features for high-dimensional text vectors, which leads to infinite KL-divergence values and creates a dilemma in assigning objects to centroids during the iteration process of Info-Kmeans. To meet this challenge, in this paper, we propose a Summation-bAsed Incremental Learning (SAIL) algorithm for Info-Kmeans clustering. Specifically, by using an equivalent objective function, SAIL replaces the computation of KL-divergence by the incremental computation of Shannon entropy. This can avoid the zero-feature dilemma caused by the use of KL-divergence. To improve the clustering quality, we further introduce the variable neighborhood search scheme and propose the V-SAIL algorithm, which is then accelerated by a multithreaded scheme in PV-SAIL. Our experimental results on various real-world text collections have shown that, with SAIL as a booster, the clustering performance of Info-Kmeans can be significantly improved. Also, V-SAIL and PV-SAIL indeed help improve the clustering quality at a lower cost of computation.

Recent years have witnessed increased interest in computing strongly correlated pairs in very large databases. Most previous studies have been focused on static data sets. However, in real-world applications, input data are often dynamic and must continually be updated. With such large and growing data sets, new research efforts are expected to develop an incremental solution for correlation computing. Along this line, in this paper, we propose a CHECK-POINT algorithm that can efficiently incorporate new transactions for correlation computing as they become available. Specifically, we set a checkpoint to establish a computation buffer, which can help us determine an upper bound for the correlation. This checkpoint bound can be exploited to identify a list of candidate pairs, which will be maintained and computed for correlations as new transactions are added into the database. However, if the total number of new transactions is beyond the buffer size, a new upper bound is computed by the new checkpoint and a new list of candidate pairs is identified. Experimental results on real-world data sets show that CHECK-POINT can significantly reduce the correlation computing cost in dynamic data sets and has the advantage of compacting the use of memory space.

Hypergraph partitioning has been considered as a promising method to address the challenges of high dimensionality in document clustering. With documents modeled as vertices and the relationship among documents captured by the hyperedges, the goal of graph partitioning is to minimize the edge cut. Therefore, the definition of hyperedges is vital to the clustering performance. While several definitions of hyperedges have been proposed, a systematic understanding of desired characteristics of hyperedges is still missing. To that end, in this paper, we first provide a unified clique perspective of the definition of hyperedges, which serves as a guide to define hyperedges. With this perspective, based on the concepts of hypercliques and shared (reverse) nearest neighbors, we propose three new types of clique hyperedges and analyze their properties regarding purity and size issues. Finally, we present an extensive evaluation using real-world document datasets. The experimental results show that, with shared (reverse) nearest neighbor based hyperedges, the clustering performance can be improved significantly in terms of various external validation measures without the need for fine tuning of parameters.

This paper targets on enhancing Latent Semantic Indexing (LSI) by exploiting category labels. Specifically, in the term-document matrix, the vector for each term either appearing in labels or semantically close to labels is scaled before performing Singular Value Decomposition (SVD) to boost its impact on the generated left singular vectors. As a result, the similarities among documents in the same category are increased. Furthermore, an adaptive scaling strategy is designed to better utilize the hierarchical structure of categories. Experimental results show that the proposed approach is able to significantly improve the performance of hierarchical text categorization.

Frequent failures are becoming a serious concern to the community of high-end computing, especially when the applications and the underlying systems rapidly grow in size and complexity. In order to develop effective fault-tolerant strategies, there is a critical need to predict failure events. To this end, we have collected detailed event logs from IBM BlueGene/L, which has 128 K processors, and is currently the fastest supercomputer in the world. In this study, we first show how the event records can be converted into a data set that is appropriate for running classification techniques. Then we apply classifiers on the data, including RIPPER (a rule-based classifier), Support Vector Machines (SVMs), a traditional Nearest Neighbor method, and a customized Nearest Neighbor method. We show that the customized nearest neighbor approach can outperform RIPPER and SVMs in terms of both coverage and precision. The results suggest that the customized nearest neighbor approach can be used to alleviate the impact of failures.

K-means is a widely used partitional clustering method. A large amount of effort has been made on finding better proximity (distance) functions for k-means. However, the common characteristics of proximity functions remain unknown. To this end, in this paper, we show that all proximity functions that fit k-means clustering can be generalized as k-means distance, which can be derived by a differentiable convex function. A general proof of sufficient and necessary conditions for k-means distance functions is also provided. In addition, we reveal that k-means has a general uniformization effect; that is, k-means tends to produce clusters with relatively balanced cluster sizes. This uniformization effect of k-means exists regardless of proximity functions. Finally, we have conducted extensive experiments on various real-world data sets, and the results show the evidence of the uniformization effect. Also, we observed that external clustering validation measures, such as entropy and variance of information (VI), have difficulty in measuring clustering quality if data have skewed distributions on class sizes.

In this paper, we introduce and study the Minimum Consistent Subset Cover (MCSC) problem. Given a finite ground set X and a constraint t, find the minimum number of consistent subsets that cover X, where a subset of X is consistent if it satisfies t. The MCSC problem generalizes the traditional set covering problem and has Minimum Clique Partition, a dual problem of graph coloring, as an instance. Many practical data mining problems in the areas of rule learning, clustering, and frequent pattern mining can be formulated as MCSC instances. In particular, we discuss the Minimum Rule Set problem that minimizes model complexity of decision rules as well as some converse k-clustering problems that minimize the number of clusters satisfying certain distance constraints. We also show how the MCSC problem can find applications in frequent pattern summarization. For any of these MCSC formulations, our proposed novel graph-based generic algorithm CAG can be directly applicable. CAG starts by constructing a maximal optimal partial solution, then performs an example-driven specific-to-general search on a dynamically maintained bipartite assignment graph to simultaneously learn a set of consistent subsets with small cardinality covering the ground set. Our experiments on benchmark datasets show that CAG achieves good results compared to existing popular heuristics.

This work studies the problem of distributed classification in peer-to-peer(P2P) networks. While there has been a significant amount of work in distributed classification, most of existing algorithms are not designed for P2P networks. Indeed, as server-less and router-less systems, P2P networks impose several challenges for distributed classification: (1) it is not practical to have global synchronization in large-scale P2P networks; (2)there are frequent topology changes caused by frequent failure and recovery of peers; and (3) there are frequent on-the-fly data updates on each peer.

Semi-supervised clustering employs limited supervision in the form of labeled instances or pairwise instance constraints to aid unsupervised clustering and often significantly improves the clustering performance. Despite the vast amount of expert knowledge spent on this problem, most existing work is not designed for handling high-dimensional sparse data. This paper thus fills this crucial void by developing a Semi-supervised Clustering method based on spheRical K-mEans via fEature projectioN (SCREEN). Specifically, we formulate the problem of constraint-guided feature projection, which can be nicely integrated with semi-supervised clustering algorithms and has the ability to effectively reduce data dimension. Indeed, our experimental results on several real-world data sets show that the SCREEN method can effectively deal with high-dimensional data and provides an appealing clustering performance.

Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature. However, the rare-class problem remains a critical challenge, because there is no natural way developed for handling imbalanced class distributions. This paper thus fills this crucial void by developing a method for Classification using lOcal clusterinG (COG). Specifically, for a data set with an imbalanced class distribution, we perform clustering within each large class and produce sub-classes with relatively balanced sizes. Then, we apply traditional supervised learning algorithms, such as Support Vector Machines (SVMs), for classification. Indeed, our experimental results on various real-world data sets show that our method produces significantly higher prediction accuracies on rare classes than state-of-the-art methods. Furthermore, we show that COG can also improve the performance of traditional supervised learning algorithms on data sets with balanced class distributions.

The claimed advantage of describing a document data set with a bipartite graph is that partitioning such a graph yields a co-clustering of words and documents. The topic of each cluster can then be represented by the top words and documents that have highest within-cluster degrees. However, such claims may fail if top words and documents are selected simply because they are very general and frequent. In addition, for those words and documents across several topics, it may not be proper to assign them to a single cluster. To that end, this paper introduces a new bipartite formulation that incorporates both word hypercliques and document hypercliques as super vertices. By co-preserving hyperclique patterns during the clustering process, our experiments on real-world data sets show that better clustering results can be obtained and the cluster topic can be more precisely identified. Also, we illustrate an application of the partitioned bipartite to search engines, returning clustered search results for keyword queries. We show that the topic of each cluster with respect to the current query can be identified more accurately with the words and documents from the patterns than with those top ones from the standard bipartite formulation.

The use of association patterns for text categorization has attracted great interest and a variety of useful methods have been developed. However, the key characteristics of pattern-based text categorization remain unclear. Indeed, there are still no concrete answers for the following two questions: First, what kind of association patterns are the best candidate for pattern-based text categorization? Second, what is the most desirable way to use patterns for text categorization? In this paper, we focus on answering the above two questions. Specifically, we show that hyperclique patterns are more desirable than frequent patterns for text categorization. Along this line, we develop an algorithm for text categorization using hyperclique patterns. The experimental results show that our method provides better performance than state-of-the-art methods in terms of both computational performance and classification accuracy.

Given a query point and a collection of spatial features, a multi-type nearest neighbor query finds the shortest tour for the query point in a way such that only one instance of each feature type is visited during the tour. For example, a tourist may be interested in finding the shortest tour which starts at a hotel and passes through a post office, a gas station, and a grocery store. The multi-type nearest query problem is different from the traditional nearest neighbor query problem, since there are many objects for each feature type and the shortest tour should pass through only one object from each feature type. In this paper, we propose R-tree based optimal solutions, which exploit a page-level upper bound for efficient computation. Also, since this problem is a generalized Traveling Salesman Problem (TSP) and is NP-hard, we provide several heuristic methods for the case that there are a large number of feature types in the data. Finally, experimental results are provided to show the strength of the proposed algorithms and design decisions related to performance tuning.

Recently, there has been considerable interest in computing strongly correlated pairs in large databases. Most previous studies require the specification of a minimum correlation threshold to perform the computation. However, it may be difficult for users to provide an appropriate threshold in practice, since different data sets typically have different characteristics. To this end, we propose an alternative task: mining the top-k strongly correlated pairs. In this paper, we identify a 2-D monotone property of an upper bound of Pearson's correlation coefficient and develop an efficient algorithm, called TOP-COP to exploit this property to effectively prune many pairs even without computing their correlation coefficients. Our experimental results show that the TOP-COP algorithm can be orders of magnitude faster than brute-force alternatives for mining the top-k strongly correlated pairs.