Large language models (LLMs) are revolutionizing user interactions in online map applications by enabling conversational interfaces with intelligent map agents. However, inherent ambiguities and nuances in human communication often lead to incomplete or unclear user instructions. While generating clarification questions can mitigate this issue, existing methods—which rely on prompt-based LLM assessments or supervised fine-tuning (SFT) of LLMs with limited annotated data—struggle to reliably determine when clarification is necessary, resulting in cold-start issues and reduced adaptability. To address this gap, we propose an uncertainty-aware dynamic planning framework for intent disambiguation in interactive agent systems, exemplified by Baidu Maps. Our framework leverages LLMs to dynamically generate agent action sequences while estimating query uncertainty. When uncertainty thresholds are exceeded, targeted clarification questions are triggered. Besides, to efficiently navigate the vast uncertainty space, we introduce a novel beam search-based pruning strategy. Crucially, the framework supports context-aware task planning without any additional model training. We evaluate our approach on Baidu Maps and a public robotic task, focusing on uncertainty management and adaptability. Experiments demonstrate that the framework achieves high precision across diverse LLMs without requiring an additional training phase, highlighting its versatility and robustness. Ablation studies confirm that the framework can significantly improve user intent recognition and task success rates.

Interactive recommendation is a typical information-seeking task that allows users to interactively express their needs through natural language and obtain personalized recommendations. Large language model-powered (LLM-powered) agents have become a new paradigm in interactive recommendations, effectively capturing users' real-time needs and enhancing personalized experiences. However, due to limited planning and generalization capabilities, existing formulations of LLM-powered interactive recommender agents struggle to effectively address diverse and complex user intents, such as intuitive, unrefined, or occasionally ambiguous requests. To tackle this challenge, we propose a novel ThoughtAugmented Interactive Recommender Agent system (TAIRA) that addresses complex user intents through distilled thought patterns. Specifically, TAIRA is designed as an LLM-powered multi-agent system featuring a manager agent that orchestrates recommendation tasks by decomposing user needs and planning subtasks, with its planning capacity strengthened through Thought Pattern Distillation (TPD), a thought-augmentation method that extracts high-level thoughts from the agent's and human experts' experiences. Through comprehensive experiments conducted across multiple designed datasets, TAIRA exhibits significantly enhanced performance compared to existing methods. Notably, TAIRA's thought augmentation strategies endow the agent system with the ability to solve complex tasks while generalizing effectively on novel tasks, validating its potential as a foundational framework for agent systems, particularly in complex user intent scenarios.

DNNs have gained widespread adoption in feature interaction recommendation models. However, there has been a longstanding debate on their roles. On one hand, some works claim that DNNs possess the ability to implicitly capture high-order feature interactions. Conversely, recent studies have highlighted the limitations of DNNs in effectively learning dot products, specifically second-order interactions, let alone higher-order interactions. In this paper, we present a novel perspective to understand the effectiveness of DNNs: their impact on the dimensional robustness of the representations. In particular, we conduct extensive experiments involving both parallel DNNs and stacked DNNs. Our evaluation encompasses an overall study of complete DNN on two feature interaction models, alongside a fine-grained ablation analysis of components within DNNs. Experimental results demonstrate that both parallel and stacked DNNs can effectively mitigate the dimensional collapse of embeddings. Furthermore, a gradient-based theoretical analysis, supported by empirical evidence, uncovers the underlying mechanisms of dimensional collapse. The code is accessible for reproduction. https://github.com/USTC-StarTeam/Dimensional-Collapse-Analysis

Learning transferable representations from unlabeled time series is crucial for improving performance in data-scarce classification. Existing self-supervised methods often operate at the point level and rely on unidirectional encoding, leading to low semantic density and a mismatch between pre-training and downstream optimization. In this paper, we propose TimeMAE, a self-supervised framework that reformulates masked modeling for time series via semantic unit elevation and decoupled representation learning. Instead of modeling individual time steps, TimeMAE segments time series into non-overlapping sub-series to form semantically enriched units, enabling more informative masked reconstruction while reducing computational cost. To address the representation discrepancy introduced by masking, we design a decoupled masked autoencoder that separately encodes visible and masked regions, avoiding artificial masked tokens in the main encoder. To guide pre-training, we introduce two complementary objectives: masked codeword classification, which discretizes sub-series semantics via a learned tokenizer and masked representation regression, which aligns continuous representations through a momentum-updated target encoder. Extensive experiments on five datasets demonstrate that TimeMAE outperforms competitive baselines, particularly in label-scarce scenarios and transfer learning scenarios. Our codes are publicly available at https://github.com/Mingyue-Cheng/TimeMAE.

Time series forecasting (TSF) traditionally relies on fast-thinking paradigms that map historical observations directly to future sequences of continuous values. While effective, such approaches often frame forecasting as a pattern-matching problem and tend to overlook explicit reasoning over temporal dynamics and contextual factors, which are critical for modeling long-range dependencies and non-stationary behaviors in real-world scenarios. Recent slow-thinking large language models (LLMs), such as OpenAI o1 and DeepSeek-R1, demonstrate strong inference-time multi-step reasoning abilities. This raises a fundamental question: can slow-thinking LLMs reason over temporal dynamics to support accurate TSF, even without task-specific training? To investigate this question, we present TimeReasoner, a systematic empirical study that reformulates TSF as a conditional reasoning process performed entirely at inference time. TimeReasoner integrates hybrid instructions consisting of task directives, timestamps, sequential values, and optional contextual features, and induces multi-step temporal reasoning in pretrained slow-thinking LLMs through chain-of-thought prompting and rollout-based reasoning strategies. Extensive experiments across diverse TSF benchmarks show that slow-thinking LLMs consistently outperform prior baselines or achieve competitive training-free forecasting performance. Beyond accuracy, we analyze how different inference-time reasoning strategies influence forecasting behaviors, highlighting both the potential and limitations of slow-thinking paradigms for TSF.

Search-based recommendation is one of the most critical application scenarios in e-commerce platforms. Users' complex search contexts—such as spatiotemporal factors, historical interactions, and current query's information—constitute an essential part of their decision-making, reflecting implicit preferences that complement explicit query terms. Modeling such rich contextual signals and their intricate associations with candidate items remains a key challenge. Although numerous efforts have been devoted to building more effective search methods, existing approaches still show limitations in integrating contextual information, which hinders their ability to fully capture user intent. To address these challenges, we propose a context-aware reasoning-enhanced generative search framework for better understanding the complicated context. Specifically, the framework first unifies heterogeneous user and item contexts into textual representations or text-based semantic identifiers and aligns them. To overcome the lack of explicit reasoning trajectories, we introduce a self-evolving post-training paradigm that iteratively combines supervised fine-tuning and reinforcement learning to progressively enhance the model's reasoning capability. In addition, we identify potential biases in existing RL algorithms when applied to search scenarios and present a debiased variant of GRPO to improve ranking performance. Extensive experiments on search log data collected from a real-world e-commerce platform demonstrate that our approach achieves superior performance compared with strong baselines, validating its effectiveness for search-based recommendation.

Science Question Answering (SQA) is an important task for evaluating models' capability to reason with scientific knowledge. However, the extensive availability of scientific information (e.g., basic concepts in biology, physics, and chemistry) in pre-trained corpora may cause large language models (LLMs) to rely more on memorized information rather than actual reasoning when answering questions. This reliance persists even with techniques like Chain-of-Thought prompting, resulting in shallow understanding and limited reasoning based on scientific knowledge. Therefore, to enhance LLMs' capacity to comprehend and apply scientific knowledge, we propose a framework calledMulti-AgentCooperation-basedKnowledgeExemplification (MCKE). Specifically, MCKE leverages knowledge alongside questions to create exemplified knowledge, promoting deeper understanding through innovative knowledge representation. To better evaluate the model's ability to reason and apply knowledge, we introduceNovSciQA, a multiple-choice question answering dataset based on newly created scientific knowledge. This dataset covers multi-subject scientific knowledge and questions that do not exist in reality, making it impossible for the model to rely on memorized answer-related information to answer questions. Experimental results show that the MCKE framework outperforms baselines, and the NovSciQA dataset effectively assesses models' knowledge understanding and application. Our code and dataset are available inhttps://anonymous.4open.science/r/MCKE-NovSciQA.

Recent years have witnessed the prosperity of Knowledge Graph-Based Recommendation System (KGRS), which enriches the representation of users, items, and entities by structural knowledge with striking improvement. Nevertheless, its unaffordable computational cost still limits researchers from exploring more sophisticated models. We observe that the bottleneck for training efficiency arises from the knowledge graph, which is plagued by the well-known issue of knowledge explosion. Recently, some works have attempted to slim the inflated KG via summarization techniques, which summarize multiple real nodes into the single virtual one. However, these summarized virtual nodes may ignore collaborative signals and thus fail to figure out the redundant nodes related to recommendation task. To this end, in this article, we propose a novel approach called KGTrimmer for knowledge graph pruning tailored for recommendation, to remove the unessential nodes while minimizing performance degradation. Specifically, we design an importance evaluator from a dual-view perspective. For the collective view, we embrace the idea of collective intelligence by extracting community consensus based on abundant collaborative signals, i.e., nodes are considered important if they attract attention of numerous users. For the holistic view, we learn a global mask to identify the valueless nodes from their inherent properties or overall popularity. With the collective and holistic importance scores, we build an end-to-end importance-aware graph neural network, which injects filtered knowledge to enhance the distillation of valuable user-item collaborative signals. Ultimately, we generate a pruned knowledge graph with lightweight, stable, and robust properties to facilitate the following-up recommendation task. Extensive experiments are conducted on three publicly available datasets to prove the effectiveness and generalizability of KGTrimmer, where it can reduce the number of triplets in KG by up to 90% without compromising performance.

Graph Neural Networks (GNNs) have been widely used across various fields under the homophily assumption that connected nodes are similar. However, in heterophilic graphs, where connected nodes tend to have dissimilar features, existing GNNs still face some limitations. From the perspective of structure, shallow GNNs could not capture the high-order node information, whereas deep GNNs may suffer from the over-smoothing problem. From the perspective of feature, the useful information of high-order similar nodes is often weakened by low-order dissimilar nodes in the feature update phase. To address the above problems, we propose a Global Structure-aware and Feature-augmented Graph Neural Network (GSF-GNN) to alleviate the limitations from the perspectives of structure and feature. Specifically, from the structure perspective, we design a Structure-based Global Propagation (SGP) module to establish global connections among nodes and adaptively adjust edge weights for message propagation. From the feature perspective, we introduce a Feature-augmented Compensatory Update (FCU) module, which employs a multi-view feature updating mechanism to enhance node features from different perspectives. Our theoretical analysis formally demonstrates the effectiveness of GSF-GNN in heterophilic graphs. Experiments on heterophilic and homophilic benchmark datasets validate the effectiveness of GSF-GNN across various graph structures. Moreover, GSF-GNN achieves stable performance across multiple layers and effectively alleviates the over-smoothing problem. Our codes are available on https://github.com/huijieliu2023/GSF-GNN .

User modeling serves as a crucial foundation for researchers to capture useful potential characteristics, playing a pivotal role in various applications such as recommender systems. One common challenge in user modeling is the cold-start problem, where interactions are notably limited for new users. To tackle this issue, the paradigm of meta-learning has been introduced to user modeling, yielding promising results. Similar to a guidebook for a new traveler, meta-learning significantly influences decision-making for new users in critical scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has become paramount. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, another question arises: How can we mitigate the influence of this factor to enhance fairness while ensuring accuracy? To this end, we introduce a novel F airness-aware A daptive S ampling framework for me T a-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Moreover, we provide theoretical guarantees demonstrating the convergence of FAST, showcasing its potential to effectively eliminate unfairness. Furthermore, to ensure model accuracy, we enhance FAST with FAST+ by introducing a hybrid sampling strategy at an individual level. This strategy prioritizes fairness and thoroughly explores important users during the sampling process, allowing for a better accuracy-fairness tradeoff. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of both FAST and FAST+ frameworks.

Cross-Domain Recommendation (CDR) can effectively alleviate the data sparsity issue in the recommendation system by transferring the source domain knowledge to the target domain. Many CDR methods try to find a mapping of latent embeddings of the overlapping nodes (e.g., the overlapping users) from source domains to target domains. However, due to the randomness of parameter initialization and model training, the embeddings from different domains may differ a lot and the inter-domain mapping can be difficult to learn. To this end, in this work, we propose a novel Similarity-Preserving Cross-Domain Recommendation ( SimCDR ) framework. Instead of trying to learn the inter-domain mapping, SimCDR aims to preserve the intra-domain similarities of users, such that the users that are close in the source domain are still close in the target domain. Specifically, we first verify that the intra-domain similarities of user embeddings can represent domain knowledge well. Then, based on this finding, we propose the SimCDR framework, which adds the auxiliary task of predicting similar users to preserve the intra-domain similarities of the source domain user embeddings, i.e., the source domain knowledge. Besides being distinct in knowledge transfer, SimCDR also supports multiple source domains and embedding compression, making it more suitable for real-world applications. We conduct thorough experiments that verify the effectiveness of the proposed method.

Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models’ (LLMs) reliability. For flexibility, agentic RAG employs autonomous, multi-round retrieval and reasoning to resolve queries. Although recent agentic RAG has improved via reinforcement learning, they often incur substantial token overhead from search and reasoning. This tradeoff prioritizes accuracy over efficiency. To address this issue, this work proposes TeaRAG, a T oken- e fficient a gentic RAG framework capable of compressing both retrieval content and reasoning steps. (1) First, the retrieved content is compressed by augmenting chunk-based semantic retrieval with a graph retrieval using concise triplets. A knowledge association graph is then built from semantic similarity and co-occurrence. Finally, Personalized PageRank is leveraged to highlight key knowledge within this graph, reducing the number of tokens per retrieval. (2) Besides, to reduce reasoning steps, Iterative Process-aware Direct Preference Optimization (IP-DPO) is proposed. Specifically, our reward function evaluates the knowledge sufficiency by a knowledge matching mechanism, while penalizing excessive reasoning steps. This design can produce high-quality preference-pair datasets, supporting iterative DPO to improve reasoning conciseness. Across six datasets, TeaRAG improves the average Exact Match by \(4\%\) and \(2\%\) while reducing output tokens by \(61\%\) and \(59\%\) on Llama3-8B-Instruct and Qwen2.5-14B-Instruct, respectively. Code is available at https://github.com/Applied-Machine-Learning-Lab/TeaRAG .

Recommendation systems have become ubiquitous tools in online platforms, providing personalized suggestions based on user–item interactions. To capture the dynamic higher-order connections between users and items, recommendation approaches based on dynamic graphs have garnered significant attention from researchers. However, existing recommendation methods based on dynamic graphs are often limited by data sparsity, which prevents them from achieving satisfactory performance. Fortunately, the rapid development of large language models (LLMs) with powerful text generation capabilities and extensive domain knowledge has offered new possibilities for addressing this challenge. However, how to effectively integrate LLMs with dynamic graphs remains unexplored. To bridge this gap, in this article, we propose a novel framework, that is, DynLLM, for applying LLMs to dynamic graph-based recommendation methods. Specifically, DynLLM harnesses the power of LLMs to generate multi-faceted user profiles based on the rich textual features of historical purchase records, which in turn supplement and enrich the underlying relationships between users and items. Along this line, to fuse the multi-faceted profiles with temporal graph embedding, we engage LLMs to derive corresponding profile embeddings and further employ a distilled attention mechanism to refine the LLM-generated profile embeddings for alleviating noisy signals, while also assessing and adjusting the relevance of each distilled facet embedding for seamless integration with temporal graph embedding from continuous time dynamic graphs (CTDGs). Extensive experiments on three real datasets have validated the superior improvements of DynLLM over a wide range of state-of-the-art baseline methods. The implementation code is available online at https://github.com/meteor-gif/DynLLM .

Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large language models (LLMs) through the integration of external knowledge. While numerous efforts, most studies focus on a single type of external knowledge source. However, in real-world applications, most situations involve diverse knowledge from various sources, yet this area has been less explored. The main dilemma is the lack of a suitable dataset containing multiple knowledge sources and pre-exploration of the associated issues. To address these challenges, we standardize a benchmark dataset that combines structured and unstructured knowledge across diverse and complementary domains. Based on this dataset, we further develop a plug-and-play RAG framework, PruningRAG, whose main characteristic is the use of multi-granularity pruning strategies to optimize the integration of relevant information while minimizing misleading context. It consistently improves performance across various existing RAG variants, demonstrating its robustness and broad applicability. Building upon the standardized dataset and PruningRAG, we also report a series of experimental results, as well as insightful findings. Our dataset and code are publicly available. https://github.com/USTCAGI/PruningRAG, with the aim of advancing future research in the RAG community.

Electrocardiogram (ECG) diagnosis aims to automatically classify ECG recordings into clinically meaningful categories, playing a vital role in medical decision-making. Deep learning methods, while promising, demand extensive annotated data and lack interpretability. Large Language Models (LLMs) offer potential in low-data scenarios and generating interpretable outputs, yet their application to ECG diagnosis, especially leveraging multimodal data (e.g., raw signals, derived features, and clinical knowledge), remains underexplored. To address these challenges, we propose ECG-Doctor, an interpretable and multimodal ECG diagnosis framework based on LLMs. ECG-Doctor comprises four key components: (1) ECG Knowledge Acquisition Module, which integrates external medical knowledge and Chain-of-Thought (CoT) reasoning to address the inability of LLMs to follow standardized ECG diagnostic procedures; (2) ECG Feature Extraction Module, which incorporates domain knowledge to overcome LLMs' limitations in comprehensively understanding structured ECG features; (3) ECG Waveform Analysis Module, which introduces time-series ECG models to equip LLMs with the capability to interpret and reason over raw ECG signal morphologies; (4) KNN-based ECG Retrieval Module, which retrieves the top-k most similar ECG samples and guides LLMs through in-context learning (ICL), enabling them to differentiate and learn from variations across ECGs. The outputs of these modules are aggregated and provided to the LLM as diagnostic context, enabling ICL to perform comprehensive ECG diagnosis. This design effectively simulates the diagnostic reasoning process of experienced electrocardiologists. Extensive experiments on the PTB-XL dataset demonstrate that ECG-Doctor is compatible with various LLMs and consistently outperforms existing baselines at both 100 Hz and 500 Hz sampling rates, showcasing its strong versatility and robustness. Furthermore, ECG-Doctor provides well-grounded diagnostic explanations, highlighting its superior interpretability.

Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely lowresource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. Large Language Models (LLMs) have demonstrated competitive performance with the help of their rich prior knowledge and excellent in-context learning abilities. However, existing methods face significant limitations, such as the Understanding Ambiguity and Information Scarcity, which significantly undermine the potential of LLMs. To address these shortcomings, we propose a Dual-perspective Knowledge-guided Fake News Detection (DKFND) model, designed to enhance LLMs from both inside and outside perspectives. Specifically, DKFND first identifies the knowledge concepts of each news article through a Detection Module. Subsequently, DKFND creatively designs an Investigation Module to retrieve inside and outside valuable information concerning to the current news, followed by another Judge Module to evaluate the relevance and confidence of them. Finally, a Determination Module further derives two respective predictions and obtain the final result. Extensive experiments on two public datasets show the efficacy of our proposed method, particularly in low-resource settings.

Recent advances in machine learning, particularly in Natural Language Processing (NLP), have produced powerful models trained on vast datasets. However, these models risk leaking sensitive information, raising privacy concerns. In response, regulatory measures such as the European Union's General Data Protection Regulation (GDPR) have driven increasing interest in Machine Unlearning techniques, which enable models to selectively forget specific data entries. Early unlearning approaches primarily relied on pre-processing methods, while more recent research has shifted towards training-based solutions. Despite their effectiveness, a key limitation persists: most methods require access to original training data, which is often unavailable. Additionally, directly applying unlearning techniques bears the cost of undermining the model's expressive capabilities. To address these challenges, we introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components: A Knowledge Unlearning Induction module designed to target specific knowledge for removal using an unlearning loss; A Contrastive Learning Enhancement module to preserve the model's expressive capabilities against the pure unlearning goal; And an Iterative Unlearning Refinement module that dynamically adjusts the unlearning process through ongoing evaluation and updates. Experimental results demonstrate the efficacy of our ICU method in unlearning sensitive information while maintaining the model's overall performance, offering a promising solution for privacy-conscious machine learning applications.

Zero-shot composed image retrieval (ZS-CIR) is a rapidly growing area with significant practical applications, allowing users to retrieve a target image by providing a reference image and a relative caption describing the desired modifications. Existing ZS-CIR methods often struggle to capture fine-grained changes and integrate visual and semantic information effectively. They primarily rely on either transforming the multimodal query into a single text using image-to-text models or employing large language models for target image description generation, approaches that often fail to capture complementary visual information and complete semantic context. To address these limitations, we propose a novel Fine-Grained Zero-Shot Composed Image Retrieval method with Complementary VisualSemantic Integration (CVSI). Specifically, CVSI leverages three key components: (1) Visual Information Extraction, which not only extracts global image features but also uses a pre-trained mapping network to convert the image into a pseudo token, combining it with the modification text and the objects most likely to be added. (2) Semantic Information Extraction, which involves using a pre-trained captioning model to generate multiple captions for the reference image, followed by leveraging an LLM to generate the modified captions and the objects most likely to be added. (3) Complementary Information Retrieval, which integrates information extracted from both the query and database images to retrieve the target image, enabling the system to efficiently handle retrieval queries in a variety of situations. Extensive experiments on three public datasets (e.g., CIRR, CIRCO, and FashionIQ) demonstrate that CVSI significantly outperforms existing state-of-the-art methods. Our code is available at https://github.com/yyc6631/CVSI.

The two-tower model has become prevalent in recommender systems for its computational efficiency and robust predictive capabilities. The model usually employs two independent neural networks to encode user and item data separately, and predicts the similarity score with inner product or cosine functions, depending on which the Top-k ranked item list is generated. The optimization process typically involves a multi-label classification objective, often guided by surrogate loss functions like Softmax and One-vs-All (OvA), to enhance the recommendation performance. Despite both Softmax and OvA losses being Bayes-consistent, empirical observations reveal a significant performance gap in evaluation metrics, suggesting limitations in Bayes-consistency for analyzing loss effectiveness. To address this, we introduce ℋ-consistency into the discussion, which provides non-asymptotic and hypothesis-specific guarantees for Top-k classification within the two-tower model's hypothesis space. Through theoretical analysis, we demonstrate that Softmax and Cosine Contrastive Loss exhibit ℋ-consistency, while the OvA loss does not, explaining the observed performance discrepancies. Our findings bridge the gap between theoretical properties and practical outcomes, offering deeper insights into the optimization of two-tower models and contributing to the development of more effective recommendation systems.

Sequential recommendation (SR), as an important branch of recommendation systems, has garnered significant attention due to its substantial commercial value. This has inspired some researchers to draw from the successful experiences of large language models to develop scaling laws for SR. However, the improvements brought by parameter expansion often reach a limit when the data scale is fixed. We have observed that existing deep learning sequence methods are typically seen as learning a unified pattern of user interactions, as they apply the same model for inference across different users, which often leads to the neglect of individual user behavior patterns. To address this, we propose conducting an independent analysis of each user's interaction sequence in SR. We initially developed the PCRec-simple, which uses KL divergence to perform a one-time optimization on each sequence after training, demonstrating that optimizing individual sequences can provide additional insights and overcome the performance bottleneck after scaling laws. Subsequently, we introduce PCRec, a sequential recommendation model that integrates real-time inference of hidden states into the model. It applies KL divergence optimization during the forward process, allowing for end-to-end optimization and addressing issues of robustness, parallelism, and optimization stability. Extensive experiments on real-world datasets show that PCRec significantly outperforms the current state-of-the-art methods. The code can be found at https://github.com/USTC-StarTeam/PCRec.

Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction. Current long-term user behavior modeling algorithms predominantly follow two cascading stages. The first stage retrieves subsequence related to the target item from the long-term behavior sequence, while the second stage models the relationship between the subsequence and the target item. Despite significant progress, these methods have two critical flaws. First, the retrieval query typically includes only target item information, limiting the ability to capture the user's diverse interests. Second, relational information, such as sequential and interactive information within the subsequence, is frequently overlooked. Therefore, it requires to be further mined to more accurately model user interests.

Click-through rate (CTR) prediction is a critical task in online advertising and recommender systems, relying on effective modeling of feature interactions.Explicit interactions capture predefined relationships, such as inner products, but often suffer from data sparsity, while implicit interactions excel at learning complex patterns through non-linear transformations but lack inductive biases for efficient low-order modeling.Existing two-stream architectures integrate these paradigms but face challenges such as limited information sharing, gradient imbalance, and difficulty preserving low-order signals in sparse CTR data.We propose a novel framework, Dynamic Low-Order-Aware Fusion (DLF), which addresses these limitations through two key components: a Residual-Aware Low-Order Interaction Network (RLI) and a Network-Aware Attention Fusion Module (NAF).RLI explicitly preserves low-order signals while mitigating redundancy from residual connections, and NAF dynamically integrates explicit and implicit representations at each layer, enhancing information sharing and alleviating gradient imbalance.Together, these innovations balance low-order and high-order interactions, improving model expressiveness.Extensive experiments on public datasets demonstrate that DLF achieves

In recommendation systems, the traditional multi-stage paradigm, which includes retrieval and ranking, often suffers from information loss between stages and diminishes performance. Recent advances in generative models, inspired by natural language processing, suggest the potential for unifying these stages to mitigate such loss. This paper presents the Unified Generative Recommendation Framework (UniGRF), a novel approach that integrates retrieval and ranking into a single generative model. By treating both stages as sequence generation tasks, UniGRF enables sufficient information sharing without additional computational costs, while remaining model-agnostic. To enhance inter-stage collaboration, UniGRF introduces a ranking-driven enhancer module that leverages the precision of the ranking stage to refine retrieval processes, creating an enhancement loop. Besides, a gradient-guided adaptive weighter is incorporated to dynamically balance the optimization of retrieval and ranking, ensuring synchronized performance improvements. Extensive experiments demonstrate that UniGRF significantly outperforms existing models on benchmark datasets, confirming its effectiveness in facilitating information transfer. Ablation studies and further experiments reveal that UniGRF not only promotes efficient collaboration between stages but also achieves synchronized optimization. UniGRF provides an effective, scalable, and compatible framework for generative recommendation systems.

For the advancement of time series classification, we can summarize that most existing methods adopt a common learning-to-classify paradigm - a classifier model tries to learn the relation between sequence inputs and target label encoded by one-hot distribution. Although effective, this paradigm conceals two inherent limitations: (1) one-hot distribution fails to reflect the comparability and similarity between labels, and (2) it is difficult to learn transferable representation across domains. In this work, we propose InstructTime, a novel attempt to reshape time series classification as a learning-to-generate paradigm. Relying on the generative capacity of the pre-trained language model, the core idea is to formulate the classification of time series as a multimodal understanding task. Specifically, firstly, a time series discretization module is designed to convert continuous inputs into a sequence of discrete tokens to solve the inconsistency issue across modality data. Secondly, we introduce an alignment projected layer before feeding the transformed token of time series into language models. Thirdly, prior to fine-tuning the language model for the target domain, it is essential to emphasize the necessity of auto-regressive pre-training across various modality inputs. Finally, extensive experimentation are conducted on several prevalent public benchmark datasets, indicating the superior performance of the InstructTime. Our code is at https://github.com/Mingyue-Cheng/InstructTime.

With the proliferation of images in online content, language-guided image retrieval (LGIR) has emerged as a research hotspot over the past decade, encompassing a variety of subtasks with diverse input forms. While the development of large multimodal models (LMMs) has significantly facilitated these tasks, existing approaches often address them in isolation, requiring the construction of separate systems for each task. This not only increases system complexity and maintenance costs, but also exacerbates challenges stemming from language ambiguity and complex image content, making it difficult for retrieval systems to provide accurate and reliable results. To this end, we propose ImageScope, a training-free, three-stage framework that leverages collective reasoning to unify LGIR tasks. The key insight behind the unification lies in the compositional nature of language, which transforms diverse LGIR tasks into a generalized text-to-image retrieval process, along with the reasoning of LMMs serving as a universal verification to refine the results. To be specific, in the first stage, we improve the robustness of the framework by synthesizing search intents across varying levels of semantic granularity using chain-of-thought (CoT) reasoning. In the second and third stages, we then reflect on retrieval results by verifying predicate propositions locally, and performing pairwise evaluations globally. Experiments conducted on six LGIR datasets demonstrate that ImageScope outperforms competitive baselines. Comprehensive evaluations and ablation studies further confirm the effectiveness of our design.

In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on large-scale datasets, but this also results in significant training costs. Dataset distillation has emerged as a key solution, condensing large datasets to accelerate model training while preserving model performance. However, condensing discrete and sequentially correlated user-item interactions, particularly with extensive item sets, presents considerable challenges. This paper introduces TD3, a novel Tucker Decomposition based Dataset Distillation method within a meta-learning framework, designed for sequential recommendation. TD3 distills a fully expressive synthetic sequence summary from original data. To efficiently reduce computational complexity and extract refined latent patterns, Tucker decomposition decouples the summary into four factors: synthetic user latent factor, temporal dynamics latent factor, shared item latent factor, and a relation core that models their interconnections. Additionally, a surrogate objective in bi-level optimization is proposed to align feature spaces extracted from models trained on both original data and synthetic sequence summary beyond the naive performance matching approach. In the inner-loop, an augmentation technique allows the learner to closely fit the synthetic summary, ensuring an accurate update of it in the outer-loop. To accelerate the optimization process and address long dependencies, RaT-BPTT is employed for bi-level optimization. Experiments and analyses on multiple public datasets have confirmed the superiority and cross-architecture generalizability of the proposed designs. Codes are released at https://github.com/USTC-StarTeam/TD3.

Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly zero-shot (ZS) learning, which seeks to identify unseen concepts through the use of side information. Previous ZS studies primarily focused on utilizing existing knowledge to infer unseen events, yet they overlook the crucial process of knowledge discovery and the integrated modeling of these knowledge-aware processes. In this study, we present a comprehensive ZS learning approach that explores and evaluates the machine’s abilities of discovering and utilizing knowledge. More specifically, to emulate human-like knowledge discovery and utilization processes, we propose a novel visual-aware ZS knowledge graph completion task for evaluation, incorporating a traditional ZS image classification task. Technically, we develop a unified ZS learning paradigm named Cognitive Learner (CoLa) to foster the two knowledge-aware abilities. Including a knowledge representation learning (KRL) module and a knowledge adaptation (KA) module, CoLa adapts well to the two specified tasks with the corresponding data. Extensive experiments on large-scale datasets demonstrate CoLa models’ outstanding performance over compared methods in the two ZS tasks, illustrating their superior ability of discovering and utilizing knowledge.

Math word problem (MWP) serves as a critical milestone for assessing the text mining ability and knowledge mastery level of models. Recent advancements have witnessed large language models (LLMs) showcasing remarkable performance on MWP. However, current LLMs still frequently exhibit logical errors, which highlights their inability to fully grasp the knowledge required for genuine step-by-step mathematical reasoning. To this end, in this paper, we propose a novel Knowledge-guided Solver (KNOS) framework that empowers LLMs to simulate human mathematical reasoning, whose core idea is toInvoke-Verify-Injectnecessary knowledge to solve MWP. We draw inspiration from the dual-process theory to construct two cooperative systems: aKnowledge Systemand anInference System. Specifically, theKnowledge Systememploys LLMs as the knowledge base and develops a novelknowledge invokerthat can elicit their relevant knowledge to support the strict step-level mathematical reasoning. In theInference System, we propose aknowledge verifierand aknowledge injectorto evaluate the knowledge rationality and further guide the step-wise symbolic deduction in an interpretable manner based on human cognitive mechanism, respectively. Moreover, to tackle the potential scarcity issue of mathematics-specific knowledge in LLMs, we consider an open-book exam scenario and propose an improved version of KNOS called EKNOS. In EKNOS, we meticulously designknowledge selectorsto extract the most relevant commonsense and math formulas from external knowledge sources for each reasoning step. This knowledge is utilized to assist theknowledge invokerin better stimulating LLMs’ reasoning abilities. Both KNOS and EKNOS are flexible to empower different LLMs. Our experiments with GPT3, ChatGPT, and GPT4 not only demonstrate their reasoning accuracy improvement but also show how they bring the strict step-wise interpretability of mathematical thinking.

Knowledge Tracing (KT) predicts future performance by modeling students' historical interactions, and understanding students' affective states can enhance the effectiveness of KT, thereby improving the quality of education. Although traditional KT values students' cognition and learning behaviors, efficient evaluation of students' affective states and their application in KT still require further exploration due to the non-affect-oriented nature of the data and budget constraints. To address this issue, we propose a computation-driven approach,DynamicAffectSimulationKnowledgeTracing (DASKT), to explore the impact of various student affective states (such as frustration, concentration, boredom, and confusion) on their knowledge states. In this model, we first extract affective factors from students' non-affect-oriented behavioral data, then use clustering and spatiotemporal sequence modeling to accurately simulate students' dynamic affect changes when dealing with different problems. Subsequently, we incorporate affect with time-series analysis to improve the model's ability to infer knowledge states over time and space. Extensive experimental results on two public real-world educational datasets show that DASKT can achieve more reasonable knowledge states under the effect of students' affective states. Moreover, DASKT outperforms the most advanced KT methods in predicting student performance. Our research highlights a promising avenue for future KT studies, focusing on achieving high interpretability and accuracy.

Retrieval-augmented generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This method addresses common LLM limitations, including outdated information and the tendency to produce inaccurate “hallucinated” content. However, evaluating RAG systems is a challenge. Most benchmarks focus primarily on question-answering applications, neglecting other potential scenarios where RAG could be beneficial. Accordingly, in the experiments, these benchmarks often assess only the LLM components of the RAG pipeline or the retriever in knowledge-intensive scenarios, overlooking the impact of external knowledge base construction and the retrieval component on the entire RAG pipeline in non-knowledge-intensive scenarios. To address these issues, this article constructs a large-scale and more comprehensive benchmark and evaluates all the components of RAG systems in various RAG application scenarios. Specifically, we refer to the CRUD actions that describe interactions between users and knowledge bases and also categorize the range of RAG applications into four distinct types—create, read, update, and delete (CRUD). “Create” refers to scenarios requiring the generation of original, varied content. “Read” involves responding to intricate questions in knowledge-intensive situations. “Update” focuses on revising and rectifying inaccuracies or inconsistencies in pre-existing texts. “Delete” pertains to the task of summarizing extensive texts into more concise forms. For each of these CRUD categories, we have developed different datasets to evaluate the performance of RAG systems. We also analyze the effects of various components of the RAG system, such as the retriever, context length, knowledge base construction, and LLM. Finally, we provide useful insights for optimizing the RAG technology for different scenarios. The source code is available at GitHub: https://github.com/IAAR-Shanghai/CRUD_RAG .

Programmatic Weak Supervision (PWS) is a recent data labeling paradigm, which employs several Labeling Functions (LFs) to provide weak labels and involves a Label Model (LM) for label aggregation. Despite the significant progress, there still remain some inherent challenges in PWS. From the view of labeling, LFs may wrongly label some data points. From the view of data, some data points themselves may be low-quality (e.g., ambiguous texts or blurred images). These largely stem from the lack of an explicit evaluation mechanism for LFs or data points. To this end, inspired by confident learning focusing on label quality, we propose a Confident PWS (CPWS) approach for high-quality data labeling. Specifically, several LFs are firstly utilized to provide weak labels for unlabeled data. Then, we develop an explicit Dual Evaluation Mechanism (DEM) to evaluate the quality of both LFs and data points, which not only employs data to evaluate trained models but also leverages trained models to evaluate data. Along this line, we further design a Distribution-Guided Pruning Strategy (DPS) to prune low-quality data and aggregate weak labels under the guidance of label class distribution. Extensive experiments on various benchmark datasets demonstrate the effectiveness and generalization ability of our proposed approach.

Recently, the dual-target cross-domain recommendation has been an emerging research problem, which aims to improve the performances of both source and target domains by transferring the preferences of overlapping users. Most of the existing work adopted a coarse-grained manner to detach general users’ preferences and associate them with domain-specific information for enhancing user representation learning, which fails to depict the differences in users’ diverse preferences and aggregate relevant preferences with improper propagation. To this end, in this article, we propose a multi-factor user representation pre-training framework, dubbed MF-GSLAE, with a focus on fine-grained preference learning and transferring. Specifically, we first propose a fine-grained factor representation pre-training paradigm. It projects the behavior records of both domains into several subspaces and introduces a compactness regularization to generate multiple fine-grained preference factors. Furthermore, we propose a multi-factor graph structure learning method within linear complexity to efficiently construct preference connections on different scales of users, which could aggregate the intrinsic relationship of user preferences in immediate embedding spaces to capture high-order information. Following the pre-training, we subsequently design a factor selection module with the bootstrapping mechanism to adaptively choose the corresponding domain-related preferences and transfer domain-shared information through partial overlapping factors for addressing the negative transfer problem. Finally, the optimization objectives of both domains are formalized in a multi-task learning framework and derive the learned user representation in an end-to-end training manner. Extensive experimental results on several publicly available datasets have not only demonstrated the effectiveness of the learned user representations with the comparison of state-of-the-art baselines but also indicated the interpretability and robustness. The code of our work is publicly available at https://github.com/USTC-StarTeam/MF-GSLAE .

The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the G raph A ugmentation E mpowered C ontrastive L earning (GAECL) for recommendation framework, which uses graph augmentation based on topological and semantic dual adaptation and global co-modeling via structural optimization to co-create contrasting views for better augmentation of the CF paradigm. Specifically, we strictly filter out unimportant topologies by reconstructing the adjacency matrix and mask unimportant attributes in nodes according to the PageRank centrality principle to generate an augmented view that filters out noisy data. Additionally, GAECL achieves global collaborative modeling through structural optimization and generates another augmented view based on the PageRank centrality principle. This helps to filter the noisy data while preserving the original semantics of the data for more effective data augmentation. Extensive experiments are conducted on five datasets to demonstrate the superior performance of our model over various recommendation models.

A well-developed recommendation system can not only leverage multi-typed interactions (such as page view , add-to-cart , and purchase ) to better identify user preferences but also demonstrate high performance, low complexity, and strong interpretability. However, many existing solutions for multi-behavior recommendation fall short of intuitive modeling of real-world scenarios, leading to overly complex models with massive parameters and cumbersome components. In particular, they share two critical limitations: (1) Some pioneering models are built upon the strict assumption of cascade effects across behaviors, which contradicts multifarious behavior paths in practical applications. (2) Existing approaches fail to explicitly capture the unique idiosyncrasies of users and even neglect the inherent nature of items involved in the multi-behavior interactions. To this end, we propose a novel Directed Acyclic Graph Convolutional Network (DA-GCN) for the multi-behavior recommendation task. Specifically, we pinpoint the partial order relations within the monotonic behavior chain and extend it to personalized directed acyclic behavior graphs to exploit behavior dependencies. Then, a GCN-based directed edge encoder is employed to distill rich collaborative signals embodied by each directed edge. In light of the information flows over the directed acyclic structure, we propose an attentive aggregation module to gather messages from all potential antecedent behaviors, representing distinct perspectives to understand the terminated behavior. Thus, we obtain comprehensive representations for the follow-up behavior through learnable distributions over its preceding behaviors, explicitly reflecting personalized interactive patterns of users and underlying properties of items simultaneously. Finally, we design a customized multi-task learning objective for flexible joint optimization. Extensive experiments on public benchmarking datasets fully demonstrate the superiority of DA-GCN with significant performance improvement and computational efficiency over a wide range of state-of-the-art methods. Our code is available at https://github.com/xizhu1022/DA-GCN .

Collaborative filtering (CF) works have demonstrated the robust capabilities of Shallow Autoencoders on implicit feedback, showcasing highly competitive performance with other reasonable approaches (e.g., iALS and VAE-CF). However, despite their dual advantages of high performance and simple construction, EASE still exhibits several major shortcomings that must be addressed. To be more precise, the scalability of EASE is limited by the number of items, which determines the storage and inversion cost of a large dense matrix; the square-loss optimization objective does not consistently meet the recommendation task’s requirement for predicting personalized rankings, resulting in suboptimal outcomes; the regularization coefficients are sensitive and require recalibration with different datasets, leading to an exhaustive and time-consuming fine-tuning process. In order to address these obstacles, we propose a novel approach called Similarity-Structure Aware Shallow Autoencoder (AutoS \(^2\) AE) that aims to enhance both recommendation accuracy and model efficiency. Our method introduces three similarity structures: Co-occurrence, KNN, and NSW graphs, which replace the large dense matrix in EASE with a sparse structure, thus facilitating model compression. Additionally, we optimize the model by incorporating a low-rank training component into the matrix and applying a weighted square loss for improved ranking-oriented approximations. To automatically tune the hyperparameters, we further design two validation losses on the validation set for guidance and update the hyperparameters using the gradients of these validation losses. Both theoretical analyses regarding the introduction of similarity structures and empirical evaluations on multiple real-world datasets demonstrate the effectiveness of our proposed method, which significantly outperforms competing baselines.

Multimodal Entity Linking (MEL) is a crucial task that aims at linking ambiguous mentions within multimodal contexts to the referent entities in a multimodal knowledge base, such as Wikipedia. Existing methods focus heavily on using complex mechanisms and extensive model tuning methods to model the multimodal interaction on specific datasets. However, these methods overcomplicate the MEL task and overlook the visual semantic information, which makes them costly and hard to scale. Moreover, these methods cannot solve the issues like textual ambiguity, redundancy, and noisy images, which severely degrade their performance. Fortunately, the advent of Large Language Models (LLMs) with robust capabilities in text understanding and reasoning, particularly Multimodal Large Language Models (MLLMs) that can process multimodal inputs, provides new insights into addressing this challenge. However, how to design a universally applicable LLMs-based MEL approach remains a pressing challenge. To this end, we propose UniMEL, a unified framework which establishes a new paradigm to process multimodal entity linking tasks using LLMs. In this framework, we employ LLMs to augment the representation of mentions and entities individually by integrating textual and visual information and refining textual information. Subsequently, we employ the embedding-based method for retrieving and re-ranking candidate entities. Then, with only ~0.26% of the model parameters fine-tuned, LLMs can make the final selection from the candidate entities. Extensive experiments on three public benchmark datasets demonstrate that our solution achieves state-of-the-art performance, and ablation studies verify the effectiveness of all modules. Our code is available at https://github.com/Javkonline/UniMEL.

Zero-Shot Relational Learning (ZSRL), strives to predict relations that have not been observed during training, presenting a considerable challenge in terms of model generalization. Existing ZSRL methods usually utilize the prior knowledge of labels (e.g., text description, ontological schema) to enable knowledge transfer by learned features. Nonetheless, these methods remain limited to calculating the surface features exhibited by relations, failing to fully explore their underlying driving factors. This leads to insufficient discrimination between the shared and distinctive inherent components among relations, which consequently impedes the cognitive understanding required for advanced reasoning. In our study, we aim to identify and utilize shared factors that widely exist in the prior knowledge of classes to learn enhanced semantic representations via shared factors composition, and develop our Factor-based ZSRL framework (FZR) with Generative Adversarial Networks (GANs) to bridge inequality between seen and unseen classes. FZR is designed to restructure the semantic space in such a way that it captures the essence of relation formation, thereby facilitating superior knowledge transfer in zero-shot scenarios. We conduct extensive experiments and evaluate our model on real-world datasets, and the results clearly demonstrate the effectiveness of the proposed model in zero-shot relational learning tasks.

Knowledge Tracing (KT) is a crucial research task for dynamically monitoring students' knowledge states, particularly in online education systems. Recently, knowledge tracing has gained significant attention and in-depth research. Most existing methods rely on students' response data for question understanding and modeling, which helps better updating students' knowledge states. Meanwhile, question ID is utilized to indicate and represent questions. However, this presents a challenge when transitioning to new, cold-start questions that few students has answered before. Also, prior work has overlooked the semantic modeling of questions, which could better assist in modeling the transfer of students' knowledge states. In this paper, we explore leveraging the power of Large Language Models (LLMs) to help understand questions for knowledge tracing, which benefits mitigating cold-start and sparse problems and modeling the transfer of students' knowledge states in a sophisticated manner. Specifically, we first design an attribute estimation module to estimate the attribute of the questions (e.g., difficulty, ability requirements, expected response time) by prompting Large Language Models. Subsequently, we have developed a question embedding module that incorporates graph attention network to effectively utilizing these attributes. Extensive experiments on various datasets demonstrate that our model outperforms existing state-of-the-art models and effectively addresses the problems of cold-start and sparsity. In addition, due to the estimation of multiple attributes of the questions, our model exhibits superior interpretability.

Sequential recommendation has attracted increasing attention due to its ability to accurately capture the dynamic changes in user interests. We have noticed that generative models, especially diffusion models, which have achieved significant results in fields like image and audio, hold considerable promise in the field of sequential recommendation. However, existing sequential recommendation methods based on diffusion models are constrained by a prior distribution limited to Gaussian distribution, hindering the possibility of introducing user-specific information for each recommendation and leading to information loss. To address these issues, we introduce the Schrödinger Bridge into diffusion-based sequential recommendation models, creating the SdifRec model. This allows us to replace the Gaussian prior of the diffusion model with the user's current state, directly modeling the process from a user's current state to the target recommendation. Additionally, to better utilize collaborative information in recommendations, we propose an extended version of SdifRec called con-SdifRec, which utilizes user clustering information as a guiding condition to further enhance the posterior distribution. Finally, extensive experiments on multiple public benchmark datasets have demonstrated the effectiveness of SdifRec and con-SdifRec through comparison with several state-of-the-art methods. Further in-depth analysis has validated their efficiency and robustness.

Model editing aims to precisely alter the behaviors of large language models (LLMs) in relation to specific knowledge, while leaving unrelated knowledge intact. This approach has proven effective in addressing issues of hallucination and outdated information in LLMs. However, the potential of using model editing to modify knowledge in the medical field remains largely unexplored, even though resolving hallucination is a pressing need in this area. Our observations indicate that current methods face significant challenges in dealing with specialized and complex knowledge in medical domain. Therefore, we propose MedLaSA, a novel Layer-wise Scalable Adapter strategy for medical model editing. MedLaSA harnesses the strengths of both adding extra parameters and locate-then-edit methods for medical model editing. We utilize causal tracing to identify the association of knowledge in neurons across different layers, and generate a corresponding scale set from the association value for each piece of knowledge. Subsequently, we incorporate scalable adapters into the dense layers of LLMs. These adapters are assigned scaling values based on the corresponding specific knowledge, which allows for the adjustment of the adapter's weight and rank. The more similar the content, the more consistent the scale between them. This ensures precise editing of semantically identical knowledge while avoiding impact on unrelated knowledge. To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge. We build two novel medical benchmarking datasets and introduce a series of challenging and comprehensive metrics. Extensive experiments on medical LLMs demonstrate the editing efficiency of MedLaSA, without affecting unrelated knowledge.

Sequential recommender systems offer personalized suggestions by modeling users' interactions chronologically to capture dynamic user interest. Existing approaches typically fail to adequately describe the dynamics of the entire recommender system, including shifts in both user interest and item availability. To address this, we propose a simple yet effective framework with three key perspectives, tailored to the dynamics of recommender system by fully exploiting the time information. Firstly, we propose a dynamic candidate set construction approach to prevent the model from learning future interactions. Secondly, assuming that user behaviors remain consistent over short terms but may evolve over long terms, we employ a interval-weighted optimization target to model the correlation of users' historical interactions. Finally, we introduce a specialized time-aware attention module to enhance recommendations within specific temporal contexts. Extensive experiments demonstrate the effectiveness and generalizability of our framework. We make our codes publicly available.

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.

Last year has witnessed the re-flourishment of tag-aware recommender systems supported by the LLM-enriched tags. Unfortunately, though large efforts have been made, current solutions may fail to describe the diversity and uncertainty inherent in user preferences with only tag-driven profiles. Recently, with the development of geometry-based techniques, e.g., box embeddings, the diversity of user preferences now could be fully modeled as the range within a box in high dimension space. However, defect still exists as these approaches are incapable of capturing high-order neighbor signals, i.e., semantic-rich multi-hop relations within the user-tag-item tripartite graph, which severely limits the effectiveness of user modeling. To deal with this challenge, in this paper, we propose a novel framework, called BoxGNN, to perform message aggregation via combinations of logical operations, thereby incorporating high-order signals. Specifically, we first embed users, items, and tags as hyper-boxes rather than simple points in the representation space, and define two logical operations, i.e., union and intersection, to facilitate the subsequent process. Next, we perform the message aggregation mechanism via the combination of logical operations, to obtain the corresponding high-order box representations. Finally, we adopt a volume-based learning objective with Gumbel smoothing techniques to refine the representation of boxes. Extensive experiments on two publicly available datasets and one LLM-enhanced e-commerce dataset have validated the superiority of BoxGNN compared with various state-of-the-art baselines. The code is released online: https://github.com/critical88/BoxGNN.

The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users. Significant efforts have been made to enhance the capabilities of SR systems. These methods typically follow the model-centric paradigm, which involves developing effective models based on fixed datasets. However, this approach often overlooks potential quality issues and flaws inherent in the data. Driven by the potential of data-centric AI, we propose a novel data-centric paradigm for developing an ideal training dataset using a model-agnostic dataset regeneration framework called DR4SR. This framework enables the regeneration of a dataset with exceptional cross-architecture generalizability. Additionally, we introduce the DR4SR+ framework, which incorporates a model-aware dataset personalizer to tailor the regenerated dataset specifically for a target model. To demonstrate the effectiveness of the data-centric paradigm, we integrate our framework with various model-centric methods and observe significant performance improvements across four widely adopted datasets. Furthermore, we conduct in-depth analyses to explore the potential of the data-centric paradigm and provide valuable insights. The code can be found at https://github.com/USTC-StarTeam/DR4SR.

Recent advancements in Large Language Models~(LLMs) have greatly enhanced the generation capabilities of dialogue systems. However, progress on emotional expression during dialogues might be still limited, especially when capturing and processing the multimodal cues for emotional expression. Therefore, it is urgent to fully adapt the multimodal understanding ability and transferability of LLMs to enhance the emotional-oriented multimodal processing capabilities. To that end, in this paper, we propose a novel Emotion-Guided Multimodal Dialogue model based on LLM, termed ELMD. Specifically, to enhance the emotional expression ability of LLMs, our ELMD customizes an emotional retrieval module, which mainly provides appropriate response demonstration for LLM in understanding emotional context. Subsequently, a two-stage training strategy is proposed, founded on previous demonstration support, to support uncovering nuanced emotions behind multimodal information and constructing natural responses. Comprehensive experiments demonstrate the effectiveness and superiority of ELMD.

Student knowledge profile is the basis for adaptive learning applications in online learning resulting from modeling the student mastery of knowledge concepts. In recent years, typical works based on knowledge tracing (KT) expect to profile students and have achieved significant success for the next performance prediction. However, in practical online learning scenarios, current methods tend to suffer from the following challenges: 1) Prediction inconsistency: The accuracy of the next performance prediction is inconsistent with the accuracy of student knowledge profile prediction, which is the more required result. 2) Cold start of knowledge: In online learning scenarios, it is often necessary to profile some knowledge concepts without learning records in advance. In this paper, we propose a novel Graph-based Student Knowledge Profile Model (GSKPM), along with a new end-to-end training objective, to tackle these challenges. We first define a new training objective to ensure the model is capable of inferring consistent student knowledge profiles. Then in this model, a two-stage hyper-aggregation process is employed to make full use of the topological relations between knowledge concepts and knowledge domains to provide information during profiling, especially for cold start knowledge concepts. Finally, through extensive experiments on real-world datasets, we will show that GSKPM achieves better prediction performances on student knowledge profiles and well deals with the cold start problem.

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.

Formula recognition endeavors to automatically identify mathematical formulas from images. Currently, the Encoder-Decoder model has significantly advanced the translation from image to corresponding formula markups. Nonetheless, previous research primarily concentrated on single-line formula recognition, ignoring the recognition of multi-line formulas, which presents additional challenges such as more stringent grammatical restrictions and two- dimensional positions. In this work, we present GAP (Grammar And Position-Aware formula recognition), a comprehensive framework designed to tackle the challenges in multi-line mathematical formula recognition. First, to overcome the limitations imposed by grammar, we design a novel Grammar Aware Contrastive Learning (GACL) module, integrating complex grammar rules into the transcription model through a contrastive learning mechanism. Furthermore, primitive contrastive learning lacks clear directions for comprehending grammar rules and can lead to unstable convergence or prolonged training cycles. To enhance training efficiency, we propose Rank-Based Sampling (RBS) specialized for multi-line formulas, which guides the learning process by the importance ranking of different grammar errors. Finally, spatial location information is critical considering the two-dimensional nature of multi-line formulas. To aid the model in keeping track of that global information, we introduced a Visual Coverage (VC) mechanism that incorporates historical attention information into the image features via a parameter-free way. To validate the effectiveness of our GAP framework, we construct a new dataset Multi-Line containing 12,002 multi-line formulas and conduct extensive experiments to show the efficacy of our GAP framework in capturing grammatical rules, enhancing recognition accuracy, and enhancing training efficiency. Codes and datasets are available at https://github.com/Sinon02/GAP.

In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to cart, and purchasing. Multi-behavior sequential recommendation aims to jointly consider multiple behaviors to improve the target behavior's performance. However, with diversified behavior data, user behavior sequences will become very long in the short term, which brings challenges to the efficiency of the sequence recommendation model. Meanwhile, some behavior data will also bring inevitable noise to the modeling of user interests. To address the aforementioned issues, firstly, we develop the Efficient Behavior Sequence Miner (EBM) that efficiently captures intricate patterns in user behavior while maintaining low time complexity and parameter count. Secondly, we design hard and soft denoising modules for different noise types and fully explore the relationship between behaviors and noise. Finally, we introduce a contrastive loss function along with a guided training strategy to contrast the valid information with the noisy signal in the data, and seamlessly integrate the two denoising processes to achieve a high degree of decoupling of the noisy signal. Sufficient experiments on real-world datasets demonstrate the effectiveness and efficiency of our approach in dealing with multi-behavior sequential recommendation.

Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult to overlook. Knowledge distillation provides a means for practical model compression to alleviate this delay. Nevertheless, the prevailing knowledge distillation approaches are not explicitly designed for semantic hashing. They ignore the unique search paradigm of semantic hashing, the inherent necessities of the distillation process, and the property of hash codes. In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. To ensure the effectiveness of two kinds of search paradigms in the context of semantic hashing, BRCD first aligns the semantic spaces between the teacher and student models through a contrastive knowledge distillation objective. Additionally, to eliminate noisy augmentations and ensure robust optimization, a cluster-based method within the knowledge distillation process is introduced. Furthermore, through a bit-level analysis, we uncover the presence of redundancy bits resulting from the bit independence property. To mitigate these effects, we introduce a bit mask mechanism in our knowledge distillation objective. Finally, extensive experiments not only showcase the noteworthy performance of our BRCD method in comparison to other knowledge distillation methods but also substantiate the generality of our methods across diverse semantic hashing models and backbones. The code for BRCD is available at https://github.com/hly1998/BRCD.

Knowledge-based question answering (KBQA) is a key task in natural language processing research, and also an approach to access the web data and knowledge, which requires exploiting knowledge graphs (KGs) for reasoning. In the literature, one promising solution for KBQA is to incorporate the pretrained language model (LM) with KGs by generating KG-centered pretraining corpus, which has shown its superiority. However, these methods often depend on specific techniques and resources to work, which may not always be available and restrict its application. Moreover, existing methods focus more on improving language understanding with KGs, while neglect the more important human-like complex reasoning. To this end, in this paper, we propose a general K nowledge-I njected C urriculum P retraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). Specifically, the KI module first injects knowledge into the LM by generating KG-centered pretraining corpus, and generalizes the process into three key steps that could work with different implementations for flexible application. Next, the KA module learns knowledge from the generated corpus with LM equipped with an adapter as well as keeps its original natural language understanding ability to reduce the negative impacts of the difference between the generated and natural corpus. Last, to enable the LM with complex reasoning, the CR module follows human reasoning patterns to construct three corpora with increasing difficulties of reasoning, and further trains the LM from easy to hard in a curriculum manner to promote model learning. We provide an implementation of the general framework, and evaluate the proposed KICP on four real-word datasets. The results demonstrate that our framework can achieve higher performances, and have good generalization ability to other QA tasks.

Cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users' proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis.

The field of education has undergone a significant revolution with the advent of intelligent systems and technology, which aim to personalize the learning experience, catering to the unique needs and abilities of individual learners. In this pursuit, a fundamental challenge is designing proper test for assessing the students’ cognitive status on knowledge and skills accurately and efficiently. One promising approach, referred to as Computerized Adaptive Testing (CAT), is to administrate computer-automated tests that alternately select the next item for each examinee and estimate their cognitive states given their responses to the selected items. Nevertheless, existing CAT systems suffer from inflexibility in item selection and ineffectiveness in cognitive state estimation, respectively. In this article, we propose a Model-Agnostic adaptive testing framework via Meta-leaned Gradient Embeddings, MAMGE for short, improving both item selection and cognitive state estimation simultaneously. For item selection, we design a Gradient Embedding-based Item Selector (GEIS) which incorporates the concept of gradient embeddings to represent items and selects the best ones that are both informative and representative. For cognitive state estimation, we propose a Meta-learned Cognitive State Estimator (MCSE) to automatically control the estimation process by learning to learn a proper initialization and dynamically inferred updates. Both MCSE and GEIS are inherently model-agnostic, and the two modules have an ingenious connection via meta-learned gradient embeddings. Finally, extensive experiments evaluate the effectiveness and flexibility of MAMGE.

The topic of multimodal conversation systems has recently garnered significant attention across various industries, including travel and retail, among others. While pioneering works in this field have shown promising performance, they often focus solely on context information at the utterance level, overlooking the context-aware dependencies of multimodal semantic elements like words and images. Furthermore, the ordinal information of images, which indicates the relevance between visual context and users’ demands, remains underutilized during the integration of visual content. Additionally, the exploration of how to effectively utilize corresponding attributes provided by users when searching for desired products is still largely unexplored. To address these challenges, we propose PMATE, a P osition-aware M ultimodal di A logue system with seman T ic E lements. Specifically, to obtain semantic representations at the element level, we first unfold the multimodal historical utterances and devise a position-aware multimodal element-level encoder. This component considers all images that may be relevant to the current turn and introduces a novel position-aware image selector to choose related images before fusing the information from the two modalities. Finally, we present a knowledge-aware two-stage decoder and an attribute-enhanced image searcher for the tasks of generating textual responses and selecting image responses, respectively. We extensively evaluate our model on two large-scale multimodal dialogue datasets, and the results of our experiments demonstrate that our approach outperforms several baseline methods.

Since social media such as Facebook and X (formerly known as Twitter) have permeated various aspects of daily life, people have strong incentives to influence information dissemination on these platforms and differentiate their content from the fierce competition. Existing dissemination strategies typically employ marketing techniques, such as seeking publicity through renowned actors or targeted advertising placements. Despite their various forms, most simply spread information to strengthen user impressions without conducting formal analyses of specific influence enhancement. And coupled with high costs, most fall short of expectations. To this end, we ingeniously formulate the task of social media dissemination as poisoning attacks, which influence specified content’s dissemination among target users by intervening in some users’ social media behaviors (including retweeting, following, and profile modifying). Correspondingly, we propose a novel poisoning attack, Influence-based Social Media Attack (ISMA) to generate discrete poisoning behaviors, which is difficult to achieve with existing attacks. In ISMA, we first contribute an efficient influence evaluator to quantify the spread influence of poisoning behaviors. Based on the estimated influence, we then present an imperceptible hierarchical selector and a profile modification method ProMix to select influential behaviors to poison. Notably, our attack is driven by custom attack objectives, which allows one to flexibly design different optimization goals to change the information flow, which could solve the blindness of existing influence maximization methods. Besides, behaviors such as retweeting are gentle and simple to implement. These properties make our attack more cost-effective and practical. Extensive experiments on two large-scale real-world datasets demonstrate the superiority of our method as it significantly outperforms baselines, and additionally, the proposed evaluator’s analysis of user influence provides new insights for influence maximization on social media.

By predicting a personalized ranking on a set of items, item recommendation helps users determine the information they need. While optimizing a ranking-focused loss is more in line with the objectives of item recommendation, previous studies have indicated that current sampling-based ranking methods don't always surpass non-sampling ones. This is because it is either inefficient to sample a pool of representative negatives for better generalization or challenging to gauge their contributions to ranking-focused losses accurately. To this end, we propose a novel weighted ranking loss, which weights each negative with the softmax probability based on model's predictive score. Our theoretical analysis suggests that optimizing this loss boosts the normalized discounted cumulative gain. Furthermore, it appears that this loss acts as an approximate analytic solution for adversarial training of personalized ranking. To improve optimization efficiency, we approximate the weighted ranking loss with self-normalized importance sampling and show that the loss has good generalization properties. To improve generalization, we further develop efficient cluster-based negative samplers based on clustering over item vectors, to decrease approximation error caused by the divergence between the proposal and the target distribution. Comprehensive evaluations on real-world datasets show that our methods remarkably outperform leading item recommendation algorithms.

With the explosive growth of scholarly information, researchers spend much time and effort copiously quoting authoritative works to support their ideas or motivations. We aim to alleviate this situation by proposing a citation recommendation strategy that recalls related papers for a rough idea (a piece of text, i.e., abstract, manuscript). However, the perspective of existing citation recommendations can not be well applied to our task for two defects. First, these methods neglect the reasoning of research topics, which makes the recommendation mechanism not meticulous enough and lacks explainability. For instance, they are not able to mine the hidden citing logic for the candidate paper while recommending. We fill the research gap by constructing structural topics consisting of knowledge concepts from the textual content, where reasoning paths between topics are extracted from an external knowledge graph. Second, the citation network is viewed as a crucial structural context to enhance the recommendation performance, but the new target idea does not have links to the citation network as published papers do. To simulate the prospective topological structure, our model, meanwhile, incorporates a contrastive-learning-based alignment paradigm to encourage the consistency of content embeddings and structure-oriented embeddings. We evaluate our proposed model on three real-world datasets and demonstrate that it significantly improves recommendation accuracy while providing high-quality knowledge-aware reasoning. And an interesting visual example illustrates the reasoning process when our model actually judges samples, which supports the feasibility of our topic-view learning paradigm.

Graph learning methods have achieved remarkable performance in various domains such as social recommendation, financial fraud detection, and so on. In real applications, the underlying graph is often dynamically evolving and thus, some recent studies focus on integrating the temporal topology information of graphs into the GNN for learning graph embedding. However, the robustness of training GNNs for dynamic graphs has not been discussed so far. The major reason is how to attack dynamic graph embedding still remains largely untouched, let alone how to defend against the attacks. To enable robust training of GNNs for dynamic graphs, in this paper, we investigate the problem of how to generate attacks and defend against attacks for dynamic graph embedding. Attacking dynamic graph embedding is more challenging than attacking static graph embedding as we need to understand the temporal dynamics of graphs as well as its impact on the embedding and the injected perturbations should be distinguished from the natural evolution. In addition, the defense is very challenging as the perturbations may be hidden within the natural evolution. To tackle these technical challenges, in this paper, we first develop a novel gradient-based attack method from an optimization perspective to generate perturbations to fool dynamic graph learning methods, where a key idea is to use gradient dynamics to attack the natural dynamics of the graph. Further, we borrow the idea of the attack method and integrate it with adversarial training to train a more robust dynamic graph learning method to defend against hand-crafted attacks. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed attack and defense method, where our defense method not only achieves comparable performance on clean graphs but also significantly increases the defense performance on attacked graphs.

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.

Career mobility analysis aims at understanding the occupational movement patterns of talents across distinct labor market entities, which enables a wide range of talent-centered applications, such as job recommendation, labor demand forecasting, and company competitive analysis. Existing studies in this field mainly focus on a single fixed scale, investigating either individual trajectories at the micro-level or crowd flows among market entities at the macro-level. Consequently, the intrinsic cross-scale interactions between talents and the labor market are largely overlooked. To bridge this gap, we propose UniTRep , a novel unified representation learning framework for cross-scale career mobility analysis. Specifically, we first introduce a trajectory hypergraph structure to organize the career mobility patterns in a low-information-loss manner, where market entities and talent trajectories are represented as nodes and hyperedges, respectively. Then, for learning the market-aware talent representations , we attentively propagate the node information to the hyperedges and incorporate the market contextual features into the process of individual trajectory modeling. For learning the trajectory-enhanced market representations , we aggregate the message from hyperedges associated with a specific node to integrate the fine-grained semantics of trajectories into labor market modeling. Moreover, we design two auxiliary tasks to optimize both intra-scale and cross-scale learning with a self-supervised strategy. Extensive experiments on a real-world dataset clearly validate that UniTRep can significantly outperform state-of-the-art baselines for various tasks.

The prosperity of knowledge graphs, as well as related downstream applications, has raised the urgent need for knowledge graph completion techniques that fully support knowledge graph reasoning tasks, especially under the circumstance of training data scarcity. Although large efforts have been made on solving this challenge via few-shot learning tools, they mainly focus on simply aggregating entity neighbors to represent few-shot references, whereas the enhancement from latent semantic correlation within neighbors has been largely ignored. To that end, in this article, we propose a novel few-shot learning solution named SIM, a S emantic I nteraction M atching network that applies a Transformer framework to enhance the entity representation with capturing semantic interaction between entity neighbors. Specifically, we first design an entity-relation fusion module to adaptively encode neighbors with incorporating relation representation. Along this line, Transformer layers are integrated to capture latent correlation within neighbors, as well as the semantic diversification of the support set. Finally, a similarity score is attentively estimated with the attention mechanism. Extensive experiments on two public benchmark datasets demonstrate that our model outperforms a variety of state-of-the-art methods by a significant margin.

Few-shot Named Entity Recognition (NER) task, which aims to identify and classify entities from different domains with limited training samples, has long been treated as a basic step for knowledge graph (KG) construction. Great efforts have been made on this task with competitive performance, however, they usually treat the two subtasks, namely span detection and type classification, as mutually independent, and the integrity and correlation between subtasks have been largely ignored. Moreover, prior arts may fail to absorb the coarse-grained features of entities, resulting in a semantic-insufficient representation of entity types. To that end, in this paper, we propose a Hierarchical Enhancing ProtoNet (HEProto) based on multi-task learning, which is utilized to jointly learn these two subtasks and model their correlation. Specifically, we adopt contrastive learning to enhance the span boundary information and the type semantic representations in these two subtasks. Then, the hierarchical prototypical network is designed to leverage the coarse-grained information of entities in the type classification stage, which could help the model to better learn the fine-grained semantic representations. Along this line, we construct a similarity margin loss to reduce the similarity between fine-grained entities and other irrelevant coarse-grained prototypes. Finally, extensive experiments on the Few-NERD dataset prove that our solution outperforms competitive baseline methods. The source code of HEProto is available at \hrefhttps://github.com/fanshu6hao/HEProto https://github.com/fanshu6hao/HEProto.

Computerized Adaptive Testing (CAT) arises as a promising personalized test mode in online education, targeting at revealing students' latent knowledge state by selecting test items adaptively. The item selection strategy is the core component of CAT, which searches for the best suitable test item based on students' current estimated ability at each test step. However, existing selection strategies behave in a brute-force manner, which results in the time complexity being linear to the number of items (N) in the item pool, i.e., O(N). Thus, in reality, the search latency becomes the bottleneck for CAT with a large-scale item pool. To this end, we propose a Search-Efficient Computerized Adaptive Testing framework (SECAT), which aims at enhancing CAT with an efficient selection strategy. Specifically, SECAT contains two main phases: item pool indexing and item search. In the item pool indexing phase, we apply a student-aware spatial partition method on the item pool to divide the test items into many sub-spaces, considering the adaptability of test items. In the item search phase, we optimize the traditional single-round search strategy with the asymptotic theory and propose a multi-round search strategy that can further improve the time efficiency. Compared with existing strategies, the time complexity of SECAT decreases from O(N) to O(logN). Across two real-world datasets, SECAT achieves over 200x speed up with negligible accuracy degradation.

The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the considerable achievements, existing methods usually focus on intra-sequence modeling while overlooking exploiting global collaborative information by inter-sequence modeling, resulting in inferior recommendation performance. Therefore, previous works attempt to tackle this problem with a global collaborative item graph constructed by pre-defined rules. However, these methods neglect two crucial properties when capturing global collaborative information, i.e., adaptiveness and personalization, yielding sub-optimal user representations. To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems. Specifically, we first learn an adaptive global graph among all items and capture global collaborative information with it in a self-supervised fashion, whose computational burden can be further alleviated by the proposed SVD-based accelerator. Furthermore, based on the graph, we propose to extract and utilize personalized item correlations in the form of relative positional encoding, which is a highly compatible manner of personalizing the utilization of global collaborative information. Finally, the entire framework is optimized in a multi-task learning paradigm, thus each part of APGL4SR can be mutually reinforced. As a generic framework, APGL4SR can not only outperform other baselines with significant margins, but also exhibit promising versatility, the ability to learn a meaningful global collaborative graph, and the ability to alleviate the dimensional collapse issue of item embeddings.

Researchers have successfully adapted the privacy-preserving Federated Learning (FL) to news recommendation tasks to better protect users' privacy, although typically at the cost of performance degradation due to the data heterogeneity issue. To address this issue, Personalized Federated Learning (PFL) has emerged, among which model interpolation is a promising approach that interpolates the local personalized models with the global model. However, the existing model interpolation method may not work well for news recommendation tasks for some reasons. First, it neglects the fine-grained personalization needs at both the temporal and spatial levels in news recommendation tasks. Second, due to the cold-user problem in real-world news recommendation tasks, the local personalized models may perform poorly, thus limiting the performance gain from model interpolation. To this end, we propose FINDING (Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering ), a novel personalized federated learning framework based on model interpolation. Specifically, we first propose the fine-grained model interpolation strategy which interpolates the local personalized models with the global model in a time-aware and layer-aware way. Then, to address the cold-user problem in news recommendation tasks, we adopt the group-level personalization approach where users are dynamically clustered into groups and the group-level personalized models are used for interpolation. Extensive experiments on two real-world datasets show that our method can effectively handle the above limitations of the current model interpolation method and alleviate the heterogeneity issue faced by traditional FL.

Federated learning (FL) is important for privacy-preserving services by training models without collecting raw user data. Most FL algorithms assume all data is annotated, which is impractical due to the high cost of labeling data in real applications. To alleviate the reliance on labeled data, semi-supervised federated learning (SSFL) has been proposed to utilize unlabeled data on clients to improve model performance. However, most existing methods either have privacy issues which share models trained on other clients, or generate pseudo-labels for unlabeled local datasets with the global model, which is usually biased towards the global data distribution. The latter may lead to sub-optimal accuracy of pseudo-labels, due to the gap between the local data distribution and the global model, especially in non-IID settings. In this paper, we propose a semi-supervised heterogeneous federated learning method with local knowledge enhancement, called FedLoKe, which aims to train an accurate global model from both labeled and unlabeled local data with non-IID distributions. Specifically, in FedLoKe, the server maintains a global model to capture global data distribution, and each client learns a local model to capture local data distribution. Since the distribution captured by the local model is aligned with the local data distribution, we utilize it to generate high-accuracy pseudo-labels of the unlabeled dataset for global model training. To prevent the local model from severely overfitting the small number of local labeled data, we further use the exponential moving average and apply the global model to generate pseudo-labels for local modeling training. Experiments on four datasets show the effectiveness of FedLoKe. Our code is available at: https://github.com/zcfinal/FedLoKe.

The fundamental task of intelligent educational systems is to offer adaptive learning services to students, such as exercise recommendations and computerized adaptive testing. However, optimizing required models in these systems would always encounter the collection difficulty of high-quality interaction data in practice. Therefore, establishing a student simulator is of great value since it can generate valid interactions to help optimize models. Existing advances have achieved success but generally suffer from exposure bias and overlook long-term intentions. To tackle these problems, we propose a novel Direct-Adversarial Imitation Student Simulator (DAISim) by formulating it as a Markov Decision Process (MDP), which unifies the workflow of the simulator in training and generating to alleviate the exposure bias and single-step optimization problems. To construct the intentions underlying the complex student interactions, we first propose a direct imitation strategy to mimic the interactions with a simple reward function. Then, we propose an adversarial imitation strategy to learn a rational distribution with the reward given by a parameterized discriminator. Furthermore, we optimize the discriminator in adversarial imitation in a pairwise manner, and the theoretical analysis shows that the pairwise discriminator would improve the generation quality. We conduct extensive experiments on real-world datasets, where the results demonstrate that our DAISim can simulate high-quality student interactions whose distribution is close to real distribution and can promote several downstream services.

Biological Knowledge Graphs (BKGs) can help to model complex biological systems in a structural way to support various tasks. Nevertheless, the incompleteness problem may limit the performance of existing BKGs, which still deserves new methods to reveal the missing relations. Though great efforts have been made to knowledge graph completion, existing methods are not easy to be adapted to the multimodal biological information such as molecular structures and textual descriptions. To this end, we propose a novel co-attention-based multimodal embedding framework, named CamE, for the multimodal BKG completion task. Specifically, we design a Triple Co-Attention (TCA) operator to capture and highlight the same semantic features among different modalities. Based on TCA, we further propose two components to handle multimodal fusion and multimodal entity-relation interaction, respectively. One is the multimodal TCA fusion module to achieve a multimodal joint representation for each entity in the BKG. It aims to project different modal information into a common space by capturing the same semantic features and overcoming the modality gap. The other is the relation-aware interactive TCA module to learn interactive representation by modelling the deep interaction between multimodal entities and relations. Extensive experiments on two real-world multimodal BKG datasets demonstrate that our method significantly outperforms several state-of-the-art baselines, including 10.3% and 16.2% improvement w.r.t MRR and Hits@1 metrics over its best competitors on public DRKG-MM dataset.

Collaborative filtering (CF) from implicit datasets has attracted much attention in recent years. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proven to be helpful to exploit implicit feedback, but lead to not addressing the rank-biased scenarios where positive items are supposed to be placed on the top-k positions. Although there exist listwise methods, they have low efficiency and are not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we first smooth a well-known rank-biased measure called Mean Average Precision (MAP) as a low-bound version to make it can be optimized. After that, we combined the objective functions of optimizing the MAP with pairwise comparisons. The CLAPF framework is a new hybrid model that provides the idea of utilizing a listwise measure in a pairwise way on implicit feedback.

Vector retrieval focuses on finding the k-nearest neighbors from a bunch of data points, and is widely used in a diverse set of areas such as information retrieval and recommender system. The current state-of-the-art methods represented by HNSW usually generate indexes with a big memory footprint, restricting the scale of data they can handle, except resorting to a hybrid index with external storage. The space-partitioning learned indexes, which only occupy a small memory, have made great breakthroughs in recent years. However, these methods rely on a large amount of labeled data for supervised learning, so model complexity affects the generalization.

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.

Math formulas (e.g., "distance = speed X time'') serve as one of the fundamental commonsense knowledge in human cognition, where humans naturally acquire and manipulate them in logical thinking for mathematical reasoning problems. However, existing reasoning models mainly focus on learning heuristic linguistics or patterns to generate answers, but do not pay enough attention on learning with such formula knowledge. Thus, they are not transparent (thus uninterpretable) in terms of understanding and grasping basic mathematical logic. In this paper, to promote a step forward in the domain, we first construct two datasets (Math23K-F and MAWPS-F) with precise annotations of formula usage in each reasoning step for math word problems. Especially, our datasets are refined on the benchmark datasets, and thus ensure the generality and comparability for relevant research. Then, we propose a novel Formula-mastered Solver (FOMAS) with the guidance of mastering formula knowledge to solve the problems. Specifically, we establish FOMAS with two systems drawing insight from the dual process theory, including a Knowledge System and a Reasoning System, to learn and apply formula knowledge, respectively. The Knowledge System accumulates the math formulas, where we propose a novel pretraining manner to mimic how humans grasp the mathematical logic behind them. Then, in the Reasoning System, we develop elaborate formula-guided symbol prediction and goal generation methods that retrieve the necessary formula knowledge from Knowledge System to improve both reasoning accuracy and interpretability. It organically simulates how humans conduct complex reasoning under the explicit instruction of math formulas. Experimental results prove that FOMAS has a stronger reasoning ability and achieves a more interpretable reasoning process, which verifies the necessity of introducing formula knowledge transparently.

Multimodal entity linking (MEL) task, which aims at resolving ambiguous mentions to a multimodal knowledge graph, has attracted wide attention in recent years. Though large efforts have been made to explore the complementary effect among multiple modalities, however, they may fail to fully absorb the comprehensive expression of abbreviated textual context and implicit visual indication. Even worse, the inevitable noisy data may cause inconsistency of different modalities during the learning process, which severely degenerates the performance. To address the above issues, in this paper, we propose a novel Multi-GraIned Multimodal InteraCtion Network (MIMIC) framework for solving the MEL task. Specifically, the unified inputs of mentions and entities are first encoded by textual/visual encoders separately, to extract global descriptive features and local detailed features. Then, to derive the similarity matching score for each mention-entity pair, we device three interaction units to comprehensively explore the intra-modal interaction and inter-modal fusion among features of entities and mentions. In particular, three modules, namely the Text-based Global-Local interaction Unit (TGLU), Vision-based DuaL interaction Unit (VDLU) and Cross-Modal Fusion-based interaction Unit (CMFU) are designed to capture and integrate the fine-grained representation lying in abbreviated text and implicit visual cues. Afterwards, we introduce a unit-consistency objective function via contrastive learning to avoid inconsistency and model degradation. Experimental results on three public benchmark datasets demonstrate that our solution outperforms various state-of-the-art baselines, and ablation studies verify the effectiveness of designed modules.

Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously. Recently, the Knowledge Graph (KG) has been proven as an effective scheme for handling the zero-shot task with large-scale and non-attribute data. Prior studies always embed relationships of seen and unseen objects into visual information from existing knowledge graphs to promote the cognitive ability of the unseen data. Actually, real-world knowledge is naturally formed by multimodal facts. Compared with ordinary structural knowledge from a graph perspective, multimodal KG can provide cognitive systems with fine-grained knowledge. For example, the text description and visual content can depict more critical details of a fact than only depending on knowledge triplets. Unfortunately, this multimodal fine-grained knowledge is largely unexploited due to the bottleneck of feature alignment between different modalities. To that end, we propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings via a designed dense attention module and self-calibration loss. It makes the semantic transfer process of our ZSL framework learns more differentiated knowledge between entities. Our model also gets rid of the performance limitation of only using rough global features. We conduct extensive experiments and evaluate our model on large-scale real-world data. The experimental results clearly demonstrate the effectiveness of the proposed model in standard zero-shot classification tasks.

This paper introduces a unified causal lens for understanding representative model interpretation methods. We show that their explanation scores align with the concept of average treatment effect in causal inference, which allows us to evaluate their relative strengths and limitations from a unified causal perspective. Based on our observations, we outline the major challenges in applying causal inference to model interpretation, including identifying common causes that can be generalized across instances and ensuring that explanations provide a complete causal explanation of model predictions. We then present CIMI, a Causality-Inspired Model Interpreter, which addresses these challenges. Our experiments show that CIMI provides more faithful and generalizable explanations with improved sampling efficiency, making it particularly suitable for larger pretrained models.

Exploring how learners' knowledge states evolve during the learning activities is a critical task in online learning systems, which can facilitate personalized services downstream, such as course recommendation. Most of existing methods have devoted great efforts to analyzing learners' knowledge states according to their responses (i.e., right or wrong) to different questions. However, the significant effect of learners' learning behaviors (e.g., answering speed, the number of attempts) is omitted, which can reflect their knowledge acquisition deeper and ensure the reliability of the response. In this paper, we propose a Learning Behavior-oriented Knowledge Tracing (LBKT) model, with the goal of explicitly exploring the learning behavior effects on learners' knowledge states. Specifically, we first analyze and summarize several dominated learning behaviors including Speed, Attempts and Hints in the learning process. As the characteristics of different learning behaviors vary greatly, we separately estimate their various effects on learners' knowledge acquisition in a quantitative manner. Then, considering that different learning behaviors are closely dependent with each other, we assess the fused effect of multiple learning behaviors by capturing their complex dependent patterns. Finally, we integrate the forgetting factor with learners' knowledge acquisition to comprehensively update their changing knowledge states in learning. Extensive experimental results on several public datasets demonstrate that our model generates better performance prediction for learners against existing methods. Moreover, LBKT shows good interpretability in tracking learners' knowledge state by incorporating the learning behavior effects. Our codes are available at https://github.com/xbh0720/LBKT.

Click-Through Rate (CTR) prediction of intelligent marketing systems is of great importance, in which feature interaction selection plays a key role. Most approaches model interactions of features by the same pre-defined operation under expert guidance, among which improper interactions may bring unnecessary noise and complicate the training process. To that end, in this paper, we aim to adaptively evolve the model to select proper operations to interact on feature pairs under task guidance. Inspired by natural evolution, we propose a general Cognitive EvoLutionary Search (CELS) framework, where cognitive ability refers to the malleability of organisms to orientate to the environment. Specifically, we conceptualize interactions as genomes, models as organisms, and tasks as natural environments. Mirroring how genetic malleability develops environmental adaptability, we thus diagnose the fitness of models to simulate the survival rates of organisms for natural selection, thereby an evolution path can be planned and visualized, offering an intuitive interpretation of the mechanisms underlying interaction modeling and selection. Based on the CELS framework, we develop four instantiations including individual-based search and population-based search. We demonstrate how individual mutation and population crossover enable CELS to evolve into diverse models suitable for various tasks and data, providing ready-to-use models. Extensive experiments on real-world datasets demonstrate that CELS significantly outperforms state-of-the-art approaches.

Crowdsourcing provides a practical approach for obtaining annotated data to train supervised learning models. However, since the crowd annotators may have different expertise domain and cannot always guarantee the high-quality annotations, learning from crowds generally suffers from the problem of unreliable results of introducing some noises, which makes it hard to achieve satisfying performance. In this work, we investigate the reliability of annotations to improve learning from crowds. Specifically, we first project annotator and data instance to factor vectors and model the complex interaction between annotator expertise and instance difficulty to predict annotation reliability. The learned reliability can be used to evaluate the quality of crowdsourced data directly. Then, we construct a new annotation, namely soft annotation, which serves as the gold label during the training. To recognize the different strengths of annotators, we model each annotator's confusion in an end-to-end manner. Extensive experimental results on three real-world datasets demonstrate the effectiveness of our method.

In recent years, pairwise methods, such as Bayesian Personalized Ranking (BPR), have gained significant attention in the field of collaborative filtering for recommendation systems. Group BPR is an extension of BPR that incorporates user groups to relax the strict assumption of independence between two users. However, the reliability of its user groups may be compromised as they only focus on a few behavioral similarities. To address this problem, this paper proposes a new entropy-weighted similarity measure for implicit feedback to quantify the relation between two users and sample like-minded user groups. We first introduce the group preference into several pairwise ranking algorithms and then utilize the entropy-weighted similarity to sample groups to further improve these algorithms. Unlike other approaches that rely solely on common item ratings, our method incorporates global information into the similarity measure, resulting in a more reliable approach to group sampling. We conducted experiments on two real-world datasets and evaluated our method using different metrics. The results show that our method can construct better user groups from sparse data and produce more accurate recommendations. Our approach can be applied to a wide range of recommendation systems, and this can significantly improve the performance of pairwise ranking algorithms, making it an effective tool for pairwise ranking.

A dozen recommendation libraries have recently been developed to accommodate popular recommendation algorithms for reproducibility. However, they are almost simply a collection of algorithms, overlooking the modularization of recommendation algorithms and their usage in practical scenarios. Algorithmic modularization has the following advantages: 1) helps to understand the effectiveness of each algorithm; 2) easily assembles new algorithms with well-performed modules by either drag-and-drop programming or automatic machine learning; 3) enables reinforcement between algorithms since one algorithm may act as a module of another algorithm. To this end, we develop a highly-modularized recommender system -- RecStudio, in which any recommendation algorithm is categorized into either a ranker or a retriever. In the RecStudio library, we implement 90 recommendation algorithms with the pure Pytorch, covering both common algorithms in other libraries and complex algorithms involving multiple recommendation models. RecStudio is featured from several perspectives, such as index-supported efficient recommendation and evaluation, GPU-accelerated negative sampling, hyperparameter learning on the validation, and cooperation between the retriever and ranker. RecStudio is also equipped with a web service, where the recommendation pipeline can be quickly established and visually evaluated on selected datasets, and the evaluation results are automatically archived and visualized in a leaderboard. The project and documents are released at http://recstudio.org.cn.

Automatically generating controllable and diverse mathematical word problems (MWPs) which conform to equations and topics is a crucial task in information retrieval and natural language generation. Recent deep learning models mainly focus on improving the problem readability but overlook the mathematical logic coherence, which tends to generate unsolvable problems. In this paper, we draw inspiration from the human problem-designing process and propose a Mathematical structure Planning and Knowledge enhanced Generation model (MaPKG), following the "plan-then-generate" steps. Specifically, we propose a novel dynamic planning module to make sentence-level equation plans and a dual-attention mechanism for word-level generation, incorporating equation structure representation and external commonsense knowledge. Extensive experiments on two MWP datasets show our model can guarantee more solvable, high-quality, and diverse problems. Our code is available at https://github.com/KenelmQLH/MaPKG.git

Dynamic share recommendation, which aims at recommending a friend who would like to share a particular item at a certain timestamp, has emerged as a novel task for social-oriented e-commerce platforms. Different from traditional graph-based recommendation tasks, with integrating the interconnected social interactions and fine-grained temporal information from historical share records, this novel task may encounter one unique challenge, i.e., how to deal with the dynamic social connections and asymmetric share interactions. Even worse, users may keep inactive during some periods, which results in difficulties in updating personalized profiles. To address the above challenges, in this paper, we propose a dynamic graph share recommendation model called DynShare. Specifically, we first divide each user embedding into two parts, namely the invitation embedding and vote embedding to show the tendencies of sending and receiving items, respectively. Then, temporal graph attention networks (TGATs) based on bi-directional continuous time dynamic graphs (CTDGs) are leveraged to encode temporal neighbor information from different directions. Afterward, to estimate how different users perceive the time intervals after the last interaction, we further design a time-interval aware personalized projection operator on the foundation of temporal point processes (TPPs) to project user embedding for the next-time share prediction. Extensive experiments on a real-world e-commerce share dataset have demonstrated that our proposed DynShare can achieve better results compared with state-of-the-art baseline methods. And our code is available on the project website: https://github.com/meteor-gif/DynShare.

Recent studies have demonstrated the vulnerability of recommender systems to membership inference attacks, which determine whether a user’s historical data was utilized for model training, posing serious privacy leakage issues. Existing works assumed that member and non-member users follow different recommendation modes, and then infer membership based on the difference vector between the user’s historical behaviors and the recommendation list. The previous frameworks are invalid against inductive recommendations, such as sequential recommendations, since the disparities of difference vectors constructed by the recommendations between members and non-members become imperceptible. This motivates us to dig deeper into the target model. In addition, most MIA frameworks assume that they can obtain some in-distribution data from the same distribution of the target data, which is hard to gain in recommender system.

Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task. Nevertheless, they suffer from the limitation in modeling long-range dependence due to the nature of convolution operations. Recent advancements have shown the potential of transformers to capture long-range dependence. However, it would incur severe issues, such as fixed scale representations, temporal-invariant and quadratic time complexity, with transformers directly applicable to the MTSC task because of the distinct properties of time series data. To tackle these issues, we propose FormerTime, an hierarchical representation model for improving the classification capacity for the MTSC task. In the proposed FormerTime, we employ a hierarchical network architecture to perform multi-scale feature maps. Besides, a novel transformer encoder is further designed, in which an efficient temporal reduction attention layer and a well-informed contextual positional encoding generating strategy are developed. To sum up, FormerTime exhibits three aspects of merits: (1) learning hierarchical multi-scale representations from time series data, (2) inheriting the strength of both transformers and convolutional networks, and (3) tacking the efficiency challenges incurred by the self-attention mechanism. Extensive experiments performed on 10 publicly available datasets from UEA archive verify the superiorities of the FormerTime compared to previous competitive baselines.

The Embarrassingly Shallow Autoencoders (EASE and SLIM) are strong recommendation methods based on implicit feedback, compared to competing methods like iALS and VAE-CF. However, EASE suffers from several major shortcomings. First, the training and inference of EASE can not scale with the increasing number of items since it requires storing and inverting a large dense matrix; Second, though its optimization objective – the square loss– can yield a closed-form solution, it is not consistent with recommendation goal – predicting a personalized ranking on a set of items, so that its performance is far from optimal w.r.t ranking-oriented recommendation metrics. Finally, the regularization coefficients are sensitive w.r.t recommendation accuracy and vary a lot across different datasets, so the fine-tuning of these parameters is important yet time-consuming. To improve training and inference efficiency, we propose a Similarity-Structure Aware Shallow Autoencoder on top of three similarity structures, including Co-Occurrence, KNN and NSW. We then optimize the model with a weighted square loss, which is proven effective for ranking-based recommendation but still capable of deriving closed-form solutions. However, the weight in the loss can not be learned in the training set and is similarly sensitive w.r.t the accuracy to regularization coefficients. To automatically tune the hyperparameters, we design two validation losses on the validation set for guidance, and update the hyperparameters with the gradient of the validation losses. We finally evaluate the proposed method on multiple real-world datasets and show that it outperforms seven competing baselines remarkably, and verify the effectiveness of each part in the proposed method.

In online games, predicting massive battle outcomes is a fundamental task of many applications, such as team optimization and tactical formulation. Existing works do not pay adequate attention to the massive battle. They either seek to evaluate individuals in isolation or mine simple pair-wise interactions between individuals, neither of which effectively captures the intricate interactions between massive units (e.g., individuals). Furthermore, as the team size increases, the phenomenon of diminishing marginal utility of units emerges. Such a diminishing pattern is rarely noticed in previous work, and how to capture it from data remains a challenge. To this end, we propose a novel Massive battle outcome predictor with margiNal Effect modules, namely MassNE, which comprehensively incorporates individual effects, cooperation effects (i.e., intra-team interactions) and suppression effects (i.e., inter-team interactions) for predicting battle outcomes. Specifically, we design marginal effect modules to learn how units’ marginal utility changing respect to their number, where the monotonicity assumption is applied to ensure rationality. In addition, we evaluate the current classical models and provide mathematical proofs that MassNE is able to generalize several earlier works in massive settings. Massive battle datasets generated by StarCraft II APIs are adopted to evaluate the performances of MassNE. Extensive experiments empirically demonstrate the effectiveness of MassNE, and MassNE can reveal reasonable cooperation effects, suppression effects, and marginal utilities of combat units from the data.

Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker.

Vector quantization techniques, such as Product Quantization (PQ), play a vital role in approximate nearest neighbor search (ANNs) and maximum inner product search (MIPS) owing to their remarkable search and storage efficiency. However, the indexes in vector quantization cannot be trained together with the inference models since data indexing is not differentiable. To this end, differentiable vector quantization approaches, such as DiffPQ and DeepPQ, have been recently proposed, but existing methods have two drawbacks. First, they do not impose any constraints on codebooks, such that the resultant codebooks lack diversity, leading to limited retrieval performance. Second, since data indexing resorts to operator, differentiability is usually achieved by either relaxation or Straight-Through Estimation (STE), which leads to biased gradient and slow convergence. To address these problems, we propose a Differentiable Optimized Product Quantization method (DOPQ) and beyond in this paper. Particularly, each data is projected into multiple orthogonal spaces, to generate multiple views of data. Thus, each codebook is learned with one view of data, guaranteeing the diversity of codebooks. Moreover, instead of simple differentiable relaxation, DOPQ optimizes the loss based on direct loss minimization, significantly reducing the gradient bias problem. Finally, DOPQ is evaluated with seven datasets of both recommendation and image search tasks. Extensive experimental results show that DOPQ outperforms state-of-the-art baselines by a large margin.

Knowledge Tracing (KT) aims at tracing the evolution of the knowledge states along the learning process of a learner. It has become a crucial task for online learning systems to model the learning process of their users, and further provide their users a personalized learning guidance. However, recent developments in KT based on deep neural networks mostly focus on increasing the accuracy of predicting the next performance of students. We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner’s learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. With DTransformer, we build the architecture from question-level to knowledge-level, explicitly diagnosing learner’s knowledge proficiency from each question mastery states. We also propose a novel training algorithm based on contrastive learning that focuses on maintaining the stability of the knowledge state diagnosis. Through extensive experiments, we will show that with its understanding of knowledge state evolution, DTransformer achieves a better performance prediction accuracy and more stable knowledge state tracing results. We will also show that DTransformer is less sensitive to specific patterns with case study. We open-sourced our code and data at https://github.com/yxonic/DTransformer.

In recent years, due to the explosive growth of patent applications, patent mining has drawn extensive attention and interest. An important issue of patent mining is that of recognizing the technologies contained in patents, which serves as a fundamental preparation for deeper analysis. To this end, in this article, we make a focused study on constructing a technology portrait for each patent, i.e., to recognize technical phrases concerned in it, which can summarize and represent patents from a technical perspective. Along this line, a critical challenge is how to analyze the unique characteristics of technical phrases and illustrate them with definite descriptions. Therefore, we first generate the detailed descriptions about the technical phrases existing in extensive patents based on different criteria, including various previous works, practical experience, and statistical analyses. Then, considering the unique characteristics of technical phrases and the complex structure of patent documents, such as multi-aspect semantics and multi-level relevances, we further propose a novel unsupervised model, namely TechPat, which can not only automatically recognize technical phrases from massive patents but also avoid the need for expensive human labeling. After that, we evaluate the extraction results from various aspects. Specifically, we propose a novel evaluation metric called Information Retrieval Efficiency (IRE) to quantify the performance of extracted technical phrases from a new perspective. Extensive experiments on real-world patent data demonstrate that the TechPat model can effectively discriminate technical phrases in patents and greatly outperform existing methods. We further apply extracted technical phrases to two practical application tasks, namely patent search and patent classification, where the experimental results confirm the wide application prospects of technical phrases. Finally, we discuss the generalization ability of our proposed methods.

Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCNs) can effectively learn node representations via aggregating neighbors based on the relation graph. However, despite a few exceptions, most of the previous work in this line does not consider the topical semantics underlying the edges, making the node representations less effective and the learned relation between nodes hard to explain. For instance, the current GNNs make us usually don’t know what is the reason for the connection of network nodes, such as the specific research topics cited in this article and the concerns among friends on social platforms. Some methods have begun to explore the extraction of relation semantics in recent related literature, but existing studies generally face two bottlenecks, i.e., either being unable to explain the mined latent relations to ensure their reasonableness and independence, or demanding the textual content of edges which is unavailable in most real-world datasets. Actually, these two issues are both crucial in practical use. In our work, we propose a novel Topic-Disentangled Graph Neural Network (TDG) to address the above two issues at the same time, which explores the relation topics from the perspective of node contents. We design an optimized graph topic module to handle node features to construct independent and explainable semantic subspaces, then the reasonable relation topics that correspond to these subspaces are assigned to each graph relation via a neighborhood routing mechanism. Our proposed model can be easily combined with related graph tasks to form an end-to-end model, to avoid the risk of deviation between node representation space and task space. To evaluate the efficiency of our model, sufficient node-related tasks are conducted on three public datasets in the experimental section. The results show the obvious superiority of TDG compared with the state-of-the-art models.

With the growing popularity of social media, malicious users (spammers) unfairly overpower legitimate users with unwanted or fake content to achieve their illegal purposes, which encourages research on spammer detection. The existing spammer detection methods can be characterized into feature-based detection and propagation-based detection. However, feature-based methods (e.g., GCN) cannot capture the user’s following relations, while propagation-based methods cannot utilize the rich text features. To this end, we consider combining these two methods and propose an Adaptive Reward Markov Random Field (ARMRF) layer. ARMRF layer models three intuitions on user label relations and assign them different learnable rewards. Besides, we learn the reward weights by stacking the ARMRF layer on top of GCN for end-to-end training, and we call the stacked model ARMGCN. To further improve the expressive power of ARMGCN, we propose the Markov-Driven Graph Convolutional Network (MDGCN), which integrates conditional random fields (CRF) and ARMGCN. CRF establishes the label joint probability distribution conditioned features for learning user dependencies, and the distribution can be optimized by a variational EM algorithm. We extensively evaluate the proposed method on two real-world Twitter datasets, and the experimental results demonstrate that MDGCN outperforms the state-of-the-art baselines. In addition, the ARMRF layer is model-independent, so it can be integrated with existing advanced detection methods to improve detection performance further.

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.

Information network embedding is an important way to enable efficient graph analytics. However, it still faces with computational challenges in problems such as link prediction and node recommendation, particularly with the increasing scale of networks. Both hashing and quantization are promising approaches for accelerating these problems by orders of magnitude. In the preliminary work, we have proposed to learn binary codes for information networks, but graph analytics may suffer from large accuracy degradation. To reduce information loss while achieving memory and search efficiency, we further propose to learn quantized codes for information networks. In particular, each node is represented by compositing multiple latent vectors, each of which is optimally selected from a distinct set. Since (generalized) matrix factorization unifies several well-known embedding methods with high-order proximity preserved, we propose a \underline{N}etwork \underline{R}epresentation \underline{L}ightening framework based on \underline{M}atrix \underline{F}actorization (NRL-MF) to learn binary and quantized codes. We also propose an alternating optimization algorithm for efficient parameter learning, even for the generalized matrix factorization case. We finally evaluate NRL-MF on four real-world information network datasets with respect to the tasks of node classification and node recommendation. The results show that NRL-MF significantly outperforms competing baselines in both tasks, and that quantized representations indeed incur much smaller information loss than binarized codes.

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

Reading psychology believes text comprehension to involve a complex psychological construction process, with the reader mind being a dynamic associative system that stores an abundance of schemata. For Chinese text, in particular, the unique ideographic writing system allows its lansign to trigger semantic association and schema recalling without the need of phonetics. In contrast to previous research efforts on text classification problems, in this paper we present an interdisciplinary modeling approach that draws inspirations from the cognitive principles of ideography, schema theory and deep learning to study Chinese text classification. Specifically, we first propose a Radical-guided Associative Model (RAM) for preliminary cognitive imitation, which comprises two coupled spaces, namely the Literal Space and Associative Space. Then, taking consideration of the schemata acquired from the mind of a reader which plays a important role in influencing text-dependent information revision, we extend RAM with a systematic Schema-aware Radical-guided Associative Model (SRAM) that embeds label semantics as essential text-independent human knowledge for real-world abstraction. In SRAM, the Schema Space is introduced and a Schema Attention module is proposed with a novel loss paradigm that includes the linkage and interaction between text-dependent prior concepts and text-independent label schemata. Extensive experiments on three real-world datasets demonstrate the effectiveness and rationality of our proposed method.

Cognitive diagnosis is widely applicable in the scenarios where users’ cognitive states need to be assessed, such as games and clinical measurement. Especially in intelligent education, which has become increasingly popular recent decades, cognitive diagnosis serves as a fundamental module for discovering the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manually designed function (e.g., logistic function). However, the cognitive interactions between students and exercises is a complex process, and excessive simplifications would lead to under fitting and thus get inaccurate diagnostic results. Besides, the manually designed interaction functions are relatively inflexible and limits their extensibility. This consequently causes lack of consideration about useful non-numerical information in the cognitive process besides response logs. In this article, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework as well as several implemented models (a basic implementation NeuralCDM and three extensions), where we project students and exercises to factor vectors and incorporates neural networks to learn the complex exercising interactions. To ensure the interpretability of diagnostic results, which is essential for cognitive diagnosis, we apply an monotonicity assumption to our NeuralCD framework. Moreover, NeuralCD is a general framework and has good extensibility. We show the generality of NeuralCD through proving how it can cover some traditional models. Then, we demonstrate the extensibility of NeuralCD, which benefits future developments. On one hand, we demonstrate content-based extensions where we provide examples of exploring the rich contents of exercise texts (CNCD-Q and CNCD-F). On the other hand, we demonstrate a knowledge-association based extension to show that NeuralCD is flexible for structural adjustments so as to solve specific problems. For instance, we improve the diagnostic results on uncovered knowledge concepts of a student by extending NeuralCD with the knowledge associations consideration (KaNCD). Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.

Network representation learning or network embedding has attracted wide attention due to its effectiveness on various network-oriented applications in recent years. Though large efforts have been made, they usually model the interactions between nodes reflected by network structure and attributes in a coupled way. To this end, in this article, we comprehensively study the problem of learning attributed network embedding, which focuses on characterizing different types of interactions among nodes and alleviating the sparse attribute problem as well. Specifically, we propose a novel DeCoupled Network Embedding (DCNE) model to learn node representations in a unified framework. We first respectively project both nodes and attributes into low-dimensional vectorial space. Then, we introduce a novel ‘`decoupled-fusion’' learning process into each graph layer to iteratively generate the node embeddings. In particular, we propose two adapted graph convolution modules to decouple the learning of network structure and attributes respectively, and a fusion module to adaptively aggregate the information. Next, we adopt a modified mini-batch algorithm to iteratively aggregate the higher-order information of both nodes and attributes within a multi-task learning framework. Extensive experiments on five public datasets demonstrate that DCNE could outperform state-of-the-art methods on multiple benchmark tasks.

Many data mining tasks rely on graphs to model relational structures among individuals (nodes). Since relational data are often sensitive, there is an urgent need to evaluate the privacy risks in graph data. One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns. Despite its success in grid-like domains, directly applying model inversion attacks on non-grid domains such as graph leads to poor attack performance. This is mainly due to the failure to consider the unique properties of graphs. To bridge this gap, we conduct a systematic study on model inversion attacks against Graph Neural Networks (GNNs), one of the state-of-the-art graph analysis tools in this paper. First, in the white-box setting where the attacker has full access to the target GNN model, we present GraphMI to infer the private training graph data. Specifically in GraphMI, a projected gradient module is proposed to tackle the discreteness of graph edges and preserve the sparsity and smoothness of graph features; a graph auto-encoder module is used to efficiently exploit graph topology, node attributes, and target model parameters for edge inference; a random sampling module can finally sample discrete edges. Furthermore, in the hard-label black-box setting where the attacker can only query the GNN API and receive the classification results, we propose two methods based on gradient estimation and reinforcement learning (RL-GraphMI). With the proposed methods, we study the connection between model inversion risk and edge influence and show that edges with greater influence are more likely to be recovered. Extensive experiments over several public datasets demonstrate the effectiveness of our methods. We also evaluate our attacks under two defenses: one is the well-designed differential private training, and the other is graph preprocessing. Our experimental results show that such defenses are not sufficiently effective and call for more advanced defenses against privacy attacks.

Aspect-level sentiment analysis is a granular emotional classification task that refers to identifying sentiment polarities towards aspects in a sentence. Although previous research has reached a great achievement, this task remains very challenging. First, previous approaches only focus on one specific domain, which lacks the capability of transferring to other domains. Moreover, the majority of prior studies ignore the direct relationship between aspects and the corresponding sentiment words. To this end, in this paper, we propose a novel model named Efficient Adaptive Transfer Network (EATN) for aspect-level sentiment analysis which emphasizes the need of incorporating the correlation among multiple domains. The proposed EATN provides a Domain Adaptation Module (DAM) to learn common features from the sufficiently labeled source domain and to guide the classification performance in the target domain. Specifically, DAM comprises two special tasks, with one sentiment classification task aiming to learn sentiment knowledge and the other domain classification task focusing on learning domain-invariant features. Moreover, we design aspect-aware multi-head attention mechanism to capture the direct associations between the aspects and the contextual sentiment words, which is beneficial to learn the aspect-aware semantic knowledge. Extensive experiments demonstrate the effectiveness and universality of our method.

We focus on Maximum Inner Product Search (MIPS), which is an essential problem in many machine learning communities. Given a query, MIPS finds the most similar items with the maximum inner products. Methods for Nearest Neighbor Search (NNS) which is usually defined on metric space do not exhibit the satisfactory performance for MIPS problem since inner product is a non-metric function. However, inner products exhibit many good properties compared with metric functions, such as avoiding vanishing and exploding gradients. As a result, inner product is widely used in many recommendation systems, which makes efficient Maximum Inner Product Search a key for speeding up many recommendation systems. Graph-based methods for NNS problem show the superiorities compared with other class methods. Each data point of the database is mapped to a node of the proximity graph. Nearest neighbor search in the database can be converted to route on the proximity graph to find the nearest neighbor for the query. This technique can be used to solve MIPS problem. Instead of searching the nearest neighbor for the query, we search the item with a maximum inner product with query on the proximity graph. In this article, we propose a reinforcement model to train an agent to search on the proximity graph automatically for MIPS problem if we lack the ground truths of training queries. If we know the ground truths of some training queries, our model can also utilize these ground truths by imitation learning to improve the agent’s searchability. By experiments, we can see that our proposed mode which combines reinforcement learning with imitation learning shows the superiorities over the state-of-the-art methods.

Similar text search aims to find texts relevant to a given query from a database, which is fundamental in many information retrieval applications, such as question search and exercise search. Since millions of texts always exist behind practical search engine systems, a well-developed text search system usually consists of recall and ranking stages. Specifically, the recall stage serves as the basis in the system, where the main purpose is to find a small set of relevant candidates accurately and efficiently. Towards this goal, deep semantic hashing, which projects original texts into compact hash codes, can support good search performance. However, learning desired textual hash codes is extremely difficult due to the following problems. First, compact hash codes (with short length) can improve retrieval efficiency, but the demand for learning compact hash codes cannot guarantee accuracy due to severe information loss. Second, existing methods always learn the unevenly distributed codes in the space from a local perspective, leading to unsatisfactory code-balance results. Third, a large fraction of textual data contains various types of noise in real-world applications, which causes the deviation of semantics in hash codes. To this end, in this paper, we first propose a general unsupervised encoder-decoder semantic hashing framework, namely MASH (short for Memory-bAsed Semantic Hashing), to learn the balanced and compact hash codes for similar text search. Specifically, with a target of retaining semantic information as much as possible, the encoder introduces a novel relevance constraint among informative high-dimensional representations to guide the compact hash code learning. Then, we design an external memory where the hashing learning can be optimized in the global space to ensure the code balance of the learning results, which can promote search efficiency. Besides, to alleviate the performance degradation problem of the model caused by text noise, we propose an improved SMASH (short for denoiSing Memory-bAsed Semantic Hashing) model by incorporating a noise-aware encoder-decoder framework. This framework considers the noise degree for each text from the semantic deviation aspect, ensuring the robustness of hash codes. Finally, we conduct extensive experiments in three real-world datasets. The experimental results clearly demonstrate the effectiveness and efficiency of MASH and SMASH in generating balanced and compact hash codes, as well as the superior denoising ability of SMASH.

News recommender systems have become an effective manner to help users make decisions by suggesting the potential news that users may click and read, which has shown the proliferation nowadays. Many representative algorithms made great efforts to discover users’ preferences from the histories for triggering news recommendations. However, there exist some limitations due to the following two main issues. First, they mainly rely on the sufficient user data, which cannot well capture users’ temporal interests with very limited records. Second, always perceiving users’ histories for recommendation may ignore some important news (e.g., breaking news). In this article, we propose a novel Multi-factors Fusion model for news recommendation by integrating both user-dependent preference effect and user-independent timeliness effect together. First, to track the preference of a certain user, we decompose her reading history into two user-related factors, including the long-term habit and the short-term interest. Specifically, we extract her persistent habit by exploring the category effect of news that she focuses on from her whole records. Then, we characterize her temporary interests by proposing a recurrent neural network of analyzing the homogeneous relations between her latest clicked news and the candidate ones. Second, to describe the user-independent news timeliness effect, we propose a novel survival analysis model to estimate the instantaneous click probability of a certain news as the occurring probability of an event, where much sensational news tends to be picked out. Last, we fuse all effects to determine the probability of a user clicking on a certain news under the independent event assumption. We conduct extensive experiments on two real-world datasets. Experimental results demonstrate that our model can generate better news recommendations on both general scenario and cold-start scenario.

The generation of large amounts of personal data provides data centers with sufficient resources to mine idiosyncrasy from private records. User modeling has long been a fundamental task with the goal of capturing the latent characteristics of users from their behaviors. However, centralized user modeling on collected data has raised concerns about the risk of data misuse and privacy leakage. As a result, federated user modeling has come into favor, since it expects to provide secure multi-client collaboration for user modeling through federated learning. Unfortunately, to the best of our knowledge, existing federated learning methods that ignore the inconsistency among clients cannot be applied directly to practical user modeling scenarios, and moreover, they meet the following critical challenges: 1) Statistical heterogeneity . The distributions of user data in different clients are not always independently identically distributed (IID), which leads to unique clients with needful personalized information; 2) Privacy heterogeneity . User data contains both public and private information, which have different levels of privacy, indicating that we should balance different information shared and protected; 3) Model heterogeneity . The local user models trained with client records are heterogeneous, and thus require a flexible aggregation in the server; 4) Quality heterogeneity . Low-quality information from inconsistent clients poisons the reliability of user models and offsets the benefit from high-quality ones, meaning that we should augment the high-quality information during the process. To address the challenges, in this paper, we first propose a novel client-server architecture framework, namely Hierarchical Personalized Federated Learning (HPFL), with a primary goal of serving federated learning for user modeling in inconsistent clients. More specifically, the client train and deliver the local user model via the hierarchical components containing hierarchical information from privacy heterogeneity to join collaboration in federated learning. Moreover, the client updates the personalized user model with a fine-grained personalized update strategy for statistical heterogeneity. Correspondingly, the server flexibly aggregates hierarchical components from heterogeneous user models in the case of privacy and model heterogeneity with a differentiated component aggregation strategy. In order to augment high-quality information and generate high-quality user models, we expand HPFL to the Augmented-HPFL (AHPFL) framework by incorporating the augmented mechanisms, which filters out low-quality information such as noise, sparse information and redundant information. Specially, we construct two implementations of AHPFL, i.e., AHPFL-SVD and AHPFL-AE, where the augmented mechanisms follow SVD (singular value decomposition) and AE (autoencoder), respectively. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of both HPFL and AHPFL frameworks.

Recent years have witnessed the rapid accumulation of massive electronic medical records, which highly support intelligent medical services such as drug recommendation. However, although there are multiple interaction types between drugs, e.g., synergism and antagonism, which can influence the effect of a drug package significantly, prior arts generally neglect the interaction between drugs or consider only a single type of interaction. Moreover, most existing studies generally formulate the problem of package recommendation as getting a personalized scoring function for users, despite the limits of discriminative models to achieve satisfactory performance in practical applications. To this end, in this article, we propose a novel end-to-end Drug Package Generation (DPG) framework, which develops a new generative model for drug package recommendation that considers the interaction effects between drugs that are affected by patient conditions. Specifically, we propose to formulate the drug package generation as a sequence generation process. Along this line, we first initialize the drug interaction graph based on medical records and domain knowledge. Then, we design a novel message-passing neural network to capture the drug interaction, as well as a drug package generator based on a recurrent neural network. In detail, a mask layer is utilized to capture the impact of patient condition, and the deep reinforcement learning technique is leveraged to reduce the dependence on the drug order. Finally, extensive experiments on a real-world dataset from a first-rate hospital demonstrate the effectiveness of our DPG framework compared with several competitive baseline methods.

Abstractive summarization is a classic task in Natural Language Generation (NLG), which aims to produce a concise summary of the original document. Recently, great efforts have been made on sequence-to-sequence neural networks to generate abstractive sum- maries with a high level of fluency. However, prior arts mainly focus on the optimization of token-level likelihood, while the rich semantic information in documents has been largely ignored. In this way, the summarization results could be vulnerable to hallucinations, i.e., the semantic-level inconsistency between a summary and corresponding original document. To deal with this challenge, in this paper, we propose a novel fact-aware abstractive summarization model, named Entity-Relation Pointer Generator Network (ERPGN). Specially, we attempt to formalize the facts in original document as a factual knowledge graph, and then generate the high-quality summary via directly modeling consistency between summary and the factual knowledge graph. To that end, we first leverage two pointer net- work structures to capture the fact in original documents. Then, to enhance the traditional token-level likelihood loss, we design two extra semantic-level losses to measure the disagreement between a summary and facts from its original document. Extensive experi- ments on public datasets demonstrate that our ERPGN framework could outperform both classic abstractive summarization models and the state-of-the-art fact-aware baseline methods, with significant improvement in terms of faithfulness.

Civil Case Judgment Prediction (CCJP) is a fundamental task in the legal intelligence of the civil law system, which aims to automatically predict the judgment results on each plea of the plaintiff. Existing studies mainly focus on making judgment predictions only on a certain civil cause (e.g., the divorce dispute) by utilizing the fact descriptions and pleas of the plaintiff, which still suffer from the various causes and complicated legal essential elements in the real court. Thus, in this paper, we formalize CCJP as a multi-task learning problem and propose a CCJP method centering on the trial mode of essential elements, CPEE, which explores the practical judicial process and analyzes comprehensive legal essential elements to make judgment predictions. Specifically, we first construct three tasks (i.e., the predictions on the civil causes, law articles, and the final judgment on each plea) necessary for CCJP, that follow the judgment process and exploit the results of intermediate subtasks to make judgment predictions. Then we design a logic-enhanced network to predict the results of three tasks and conduct a comprehensive study of civil cases. Finally, owing to the interlinked and dependent relationships among each task, we adopt the cause prediction result to help predict law articles and incorporate them into final judgment prediction through a gate mechanism. Furthermore, since the existing dataset fails to provide sufficient case information, we construct a real-world CCJP dataset that contains various causes and comprehensive legal elements. Extensive experimental results on the dataset validate the effectiveness of our method.

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.

Deep learning has brought significant breakthroughs in sequential recommendation (SR) for capturing dynamic user interests. A series of recent research revealed that models with more parameters usually achieve optimal performance for SR tasks, inevitably resulting in great challenges for deploying them in real systems. Following the simple assumption that light networks might already suffice for certain users, in this work, we propose CANet, a conceptually simple yet very scalable framework for assigning adaptive network architecture in an input-dependent manner to reduce unnecessary computation. The core idea of CANet is to route the input user behaviors with a light-weighted router module. Specifically, we first construct the routing space with various submodels parameterized in terms of multiple model dimensions such as the number of layers, hidden size and embedding size. To avoid extra storage overhead of the routing space, we employ a weight-slicing schema to maintain all the submodels in exactly one network. Furthermore, we leverage several solutions to solve the discrete optimization issues caused by the router module. Thanks to them, CANet could adaptively adjust its network architecture for each input in an end-to-end manner, in which the user preference can be effectively captured. To evaluate our work, we conduct extensive experiments on benchmark datasets. Experimental results show that CANet reduces computation by 55 ~ 65% while preserving the accuracy of the original model. Our codes are available at https://github.conicantnamemyself/CANet.

Reasoning answers to mathematical problems requires machines to think and operate like a human to learn knowledge from mathematical data, which is one of the fundamental tasks for exploring general artificial intelligence. Most solutions focus on mimicking how humans understand problems, which generate the necessary expressions for answers. However, they are still far from enough since they ignore the core ability of humans to acquire knowledge from experience. In this paper, we propose a Cognitive Solver (CogSolver) that is capable of autonomously learning knowledge from scratch to solve mathematical problems, inspired by two cognitive science theories. Specifically, we draw one insight from the dual process theory to establish an intelligent BRAIN-ARM framework, and refer to another information processing theory to summarize the knowledge learning process into Store-Apply-Update steps. In CogSolver, the BRAIN system stores three types of mathematical knowledge, including semantics knowledge, relation knowledge, and mathematic rule knowledge. Then, the ARM system applies the knowledge in BRAIN to answer the problems. Specifically, we design a knowledge-aware module and a commutative module in ARM to improve its reasoning ability, where the knowledge is organically integrated into answer reasoning process. After solving the problems, BRAIN updates the stored knowledge according to the feedback of ARM, where we develop knowledge filters to eliminate the redundant ones and further form a more reasonable knowledge base. Our CogSolver carries out the above three steps iteratively, which behaves more like a human. We conduct extensive experiments on real-world math word problem datasets. The experimental results demonstrate the improvement in answer reasoning and clearly show how CogSolver gains knowledge from the problems, leading to superior interpretability. Our codes are available at https://github.com/bigdata-ustc/CogSolver.

The booming of multi-modal knowledge graphs (MMKGs) has raised the imperative demand for multi-modal entity alignment techniques, which facilitate the integration of multiple MMKGs from separate data sources. Unfortunately, prior arts harness multi-modal knowledge only via the heuristic merging of uni-modal feature embeddings. Therefore, inter-modal cues concealed in multi-modal knowledge could be largely ignored. To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect. Specifically, we first devise a multi-modal knowledge embedding module to extract visual, relational, and attribute features of entities to generate holistic entity representations for distinct MMKGs. During this procedure, we employ inter-modal enhancement mechanisms to integrate visual features to guide relational feature learning and adaptively assign attention weights to capture valuable attributes for alignment. Afterwards, we design a multi-modal contrastive learning module to achieve inter-modal enhancement fusion with avoiding the overwhelming impact of weak modalities. Experimental results on two public datasets demonstrate that our proposed MSNEA provides state-of-the-art performance with a large margin compared with competitive baselines.

Cognitive diagnostic assessment is a fundamental task in intelligent education, which aims at quantifying students' cognitive level on knowledge attributes. Since there exists learning dependency among knowledge attributes, it is crucial for cognitive diagnosis models (CDMs) to incorporate attribute hierarchy when assessing students. The attribute hierarchy is only explored by a few CDMs such as Attribute Hierarchy Method, and there are still two significant limitations in these methods. First, the time complexity would be unbearable when the number of attributes is large. Second, the assumption used to model the attribute hierarchy is too strong so that it may lose some information of the hierarchy and is not flexible enough to fit all situations. To address these limitations, we propose a novel Bayesian network-based Hierarchical Cognitive Diagnosis Framework (HierCDF), which enables many traditional diagnostic models to flexibly integrate the attribute hierarchy for better diagnosis. Specifically, we first use an efficient Bayesian network to model the influence of attribute hierarchy on students' cognitive states. Then we design a CDM adaptor to bridge the gap between students' cognitive states and the input features of existing diagnostic models. Finally, we analyze the generality and complexity of HierCDF to show its effectiveness in modeling hierarchy information. The performance of HierCDF is experimentally proved on real-world large-scale datasets.

Cognitive diagnosis, aiming at providing an approach to reveal the proficiency level of learners on knowledge concepts, plays an important role in intelligent education area and has recently received more and more attention. Although a number of works have been proposed in recent years, most of contemporary works acquire the traits parameters of learners and items in a transductive way, which are only suitable for stationary data. However, in the real scenario, the data is collected online, where learners, test items and interactions usually grow continuously, which can rarely meet the stationary condition. To this end, we propose a novel framework, Incremental Cognitive Diagnosis (ICD), to tailor cognitive diagnosis into the online scenario of intelligent education. Specifically, we first design a Deep Trait Network (DTN), which acquires the trait parameters in an inductive way rather than a transductive way. Then, we propose an Incremental Update Algorithm (IUA) to balance the effectiveness and training efficiency. We carry out Turning Point (TP) analysis to reduce update frequency, where we derive the minimum update condition based on the monotonicity theory of cognitive diagnosis. Meanwhile, we use a momentum update strategy on the incremental data to decrease update time without sacrificing effectiveness. Moreover, to keep the trait parameters as stable as possible, we refine the loss function in the incremental updating stage. Last but no least, our ICD is a general framework which can be applied to most of contemporary cognitive diagnosis models. To the best of our knowledge, this is the first attempt to investigate the incremental cognitive diagnosis problem with theoretical results about the update condition and a tailored incremental learning strategy. Extensive experiments demonstrate the effectiveness and robustness of our method.

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.

Due to the widespread presence of implicit feedback, recommendation based on them has been a long-standing research problem in academia and industry. However, it suffers from the extremely-sparse problem, since each user only interacts with a few items. One well-known and good-performing method is to treat each user's all uninteracted items as negative with low confidence. The method intrinsically imposes an implicit regularization to penalize large deviation of each user's preferences for uninteracted items from a constant. However, these methods have to assume a constant-rating prior to uninteracted items, which may be questionable. In this paper, we propose a novel ring-based regularization to penalize significant differences of each user's preferences between each item and some other items. The ring structure, described by an item graph, determines which other items are selected for each item in the regularization. The regularization not only averts the introduction of the prior ratings but also implicitly penalizes the remarkable preference differences for all items according to theoretical analysis. However, optimizing the recommenders with the regularization still suffers from computational challenges, so we develop a scalable alternating least square algorithm by carefully designing gradient computation. Therefore, as long as connecting each item with a sublinear/constant number of other items in the item graph, the overall learning algorithm could be comparably efficient to the existing algorithms. The proposed regularization is extensively evaluated with several public recommendation datasets, where the results show that the regularization could lead to considerable improvements in recommendation performance.

With the development of deep learning techniques, deep recommendation models also achieve remarkable improvements in terms of recommendation accuracy. However, due to the large number of candidate items in practice and the high cost of preference computation, these methods also suffer from low efficiency of recommendation. The recently proposed tree-based deep recommendation models alleviate the problem by directly learning tree structure and representations under the guidance of recommendation objectives. However, such models have two shortcomings. First, the max-heap assumption in the hierarchical tree, in which the preference for a parent node should be the maximum between the preferences for its children, is difficult to satisfy in their binary classification objectives. Second, the learned index only includes a single tree, which is different from the widely-used multiple trees index, providing an opportunity to improve the accuracy of recommendation.

Programming has become an important skill for individuals nowadays. For the demand to improve personal programming skill, tracking programming skill proficiency is getting more and more important. However, few researchers pay attention to measuring the programming skill of learners. Most of existing studies on learner capability portrait only made use of the exercise results, while the rich behavioral information contained in programming exercise process remains unused. Therefore, we propose a model that measures skill proficiency in programming exercise process named Programming Skill Tracing (PST). We designed Code Information Graph (CIG) to represent the feature of learners' solution code, and Code Tracing Graph (CTG) to measure the changes between the adjacent submissions. Furthermore, we divided programming skill into programming knowledge and coding ability to get more fine-grained assessment. Finally, we conducted various experiments to verify the effectiveness and interpretability of our PST model.

Knowledge Tracing (KT), which aims to assess students' dynamic knowledge states when practicing on various questions, is a fundamental research task for offering intelligent services in online learning systems. Researchers have devoted significant efforts to developing KT models with impressive performance. However, in existing KT methods, the related question difficulty level, which directly affects students' knowledge state in learning, has not been effectively explored and employed. In this paper, we focus on exploring the question difficulty effect on learning to improve student's knowledge state assessment and propose the DIfficulty Matching Knowledge Tracing (DIMKT) model. Specifically, we first explicitly incorporate the difficulty level into the question representation. Then, to establish the relation between students' knowledge state and the question difficulty level during the practice process, we accordingly design an adaptive sequential neural network in three stages: (1) measuring students' subjective feelings of the question difficulty before practice; (2) estimating students' personalized knowledge acquisition while answering questions of different difficulty levels; (3) updating students' knowledge state in varying degrees to match the question difficulty level after practice. Finally, we conduct extensive experiments on real-world datasets, and the results demonstrate that DIMKT outperforms state-of-the-art KT models. Moreover, DIMKT shows superior interpretability by exploring the question difficulty effect when making predictions. Our codes are available at https://github.com/shshen-closer/DIMKT.

Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines.

Click-through rate (CTR) prediction is fundamental in many industrial applications, such as online advertising and recommender systems. With the development of the online platforms, the sequential user behaviors grow rapidly, bringing us great opportunity to better understand user preferences.However, it is extremely challenging for existing sequential models to effectively utilize the entire behavior history of each user. First, there is a lot of noise in such long histories, which can seriously hurt the prediction performance. Second, feeding the long behavior sequence directly results in infeasible inference time and storage cost. In order to tackle these challenges, in this paper we propose a novel framework, which we name as User Behavior Clustering Sampling (UBCS). In UBCS, short sub-sequences will be obtained from the whole user history sequence with two cascaded modules: (i) Behavior Sampling module samples short sequences related to candidate items using a novel sampling method which takes relevance and temporal information into consideration; (ii) Item Clustering module clusters items into a small number of cluster centroids, mitigating the impact of noise and improving efficiency. Then, the sampled short sub-sequences will be fed into the CTR prediction module for efficient prediction. Moreover, we conduct a self-supervised consistency pre-training task to extract user persona preference and optimize the sampling module effectively. Experiments on real-world datasets demonstrate the superiority and efficiency of our proposed framework.

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models.

Computerized Adaptive Testing (CAT) is a promising testing mode in personalized online education (e.g., GRE), which aims at measuring student's proficiency accurately and reducing test length. The "adaptive" is reflected in its selection algorithm that can retrieve best-suited questions for student based on his/her estimated proficiency at each test step. Although there are many sophisticated selection algorithms for improving CAT's effectiveness, they are restricted and perturbed by the accuracy of current proficiency estimate, thus lacking robustness. To this end, we investigate a general method to enhance the robustness of existing algorithms by leveraging student's "multi-facet" nature during tests. Specifically, we present a generic optimization criterion Robust Adaptive Testing (RAT) for proficiency estimation via fusing multiple estimates at each step, which maintains a multi-facet description of student's potential proficiency. We further provide theoretical analyses of such estimator's desirable statistical properties: asymptotic unbiasedness, efficiency, and consistency. Extensive experiments on perturbed synthetic data and three real-world datasets show that selection algorithms in our RAT framework are robust and yield substantial improvements.

Knowledge tracing is a crucial and fundamental task in online education systems, which can predict students' knowledge state for personalized learning. Unfortunately, existing methods are domain-specific, whereas there are many domains (e.g., subjects, schools) in the real education scene and some domains suffer from the problem of lacking sufficient data. Therefore, how to exploit the knowledge in other domains, to improve the model's performance for target domain remains pretty much open. We term this problem as Domain Adaptation for Knowledge Tracing (DAKT), which aims to transfer knowledge from the source domain to the target one for knowledge tracing. In this paper, we propose a novel adaptable method, namely Adaptable Knowledge Tracing (AdaptKT), which contains three phases to explore this problem. Specifically, phase I is instance selection. Given the question texts of two domains, we train an auto-encoder to select and embed similar instances from both domains. Phase II is distribution discrepancy minimizing. After obtaining the selected instances and their linguistic representations, we train a knowledge tracing model and adopt the Maximum Mean Discrepancy (MMD) to minimize the discrepancy between the distributions of the domain-specific knowledge states. Phase III is fine-tuning of the output layer. We replace the output layer of the model that trained in phase II by a new one to make the knowledge tracing model's output dimension matches the number of knowledge concepts in the target domain. The new output layer is trained while other parameters that before it are frozen. We conduct extensive experiments on two large-scale real-world datasets, where the experimental results clearly demonstrate the effectiveness of AdaptKT for solving DAKT problem. We will public the code on the Github after the acceptance of the paper.

Recommendation is prevalently studied for implicit feedback recently, but it seriously suffers from the lack of negative samples, which has a significant impact on the training of recommendation models. Existing negative sampling is based on the static or adaptive probability distributions. Sampling from the adaptive probability receives more attention, since it tends to generate more hard examples, to make recommender training faster to converge. However, item sampling becomes much more time-consuming particularly for complex recommendation models. In this paper, we propose an Adaptive Sampling method based on Importance Resampling (AdaSIR for short), which is not only almost equally efficient and accurate for any recommender models, but also can robustly accommodate arbitrary proposal distributions. More concretely, AdaSIR maintains a contextualized sample pool of fixed-size with importance resampling, from which items are only uniformly sampled. Such a simple sampling method can be proved to provide approximately accurate adaptive sampling under some conditions. The sample pool plays two extra important roles in (1) reusing historical hard samples with certain probabilities; (2) estimating the rank of positive samples for weighting, such that recommender training can concentrate more on difficult positive samples. Extensive empirical experiments demonstrate that AdaSIR outperforms state-of-the-art methods in terms of sampling efficiency and effectiveness.

Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for collaborative filtering. However, the bottleneck of VAE lies in the softmax computation over all items, such that it takes linear costs in the number of items to compute the loss and gradient for optimization. This hinders the practical use due to millions of items in real-world scenarios. Importance sampling is an effective approximation method, based on which the sampled softmax has been derived. However, existing methods usually exploit the uniform or popularity sampler as proposal distributions, leading to a large bias of gradient estimation. To this end, we propose to decompose the inner-product-based softmax probability based on the inverted multi-index, leading to sublinear-time and highly accurate sampling. Based on the proposed proposals, we develop a fast Variational AutoEncoder (FastVAE) for collaborative filtering. FastVAE can outperform the state-of-the-art baselines in terms of both sampling quality and efficiency according to the experiments on three real-world datasets.

Recent years have witnessed great success in deep learning-based sequential recommendation (SR), which can provide more timely and accurate recommendations. One of the most effective deep SR architectures is to stack high-performance residual blocks, e.g., prevalent self-attentive and convolutional operations, for capturing long- and short-range dependence of sequential behaviors. By carefully revisiting previous models, we observe: 1) simple architecture modification of gating each residual connection can help us train deeper SR models and yield significant improvements; 2) compared with self-attention mechanism, stacking of convolution layers also can cover each item of the whole sequential behaviors and achieve competitive or even superior performance.

Time-series prediction is of high practical value in a wide range of applications such as econometrics and meteorology, where the data are commonly formed by temporal patterns. Most prior works ignore the diversity of dynamic pattern frequency, i.e., different granularities, suffering from insufficient information exploitation. Thus, multi-granularity learning is still under-explored for time-series prediction. In this paper, we propose a Multi-granularity Residual Learning Framework (MRLF) for more effective time series prediction. For a given time series, intuitively, there are more or less semantic overlaps and validity differences among its representations of different granularities. Due to the information redundancy, straightforward methods that leverage multi-granularity data, such as concatenation or ensemble, can easily lead to the model being dominated by the redundant coarse-grained trend information. Therefore, we design a novel residual learning net to model the prior knowledge of the fine-grained data’s distribution through the coarse-grained one. Then, by calculating the residual between multi-granularity data, the redundant information be removed. Furthermore, to alleviate the side effect of validity differences, we introduce a self-supervised objective for confidence estimation, which delivers more effective optimization without the requirement of additional annotation efforts. Extensive experiments on the real-world datasets indicate that multi-granular information significantly improves the time series prediction performance, and our model is superior in capturing such information.

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.

One-class collaborative filtering (OCCF) problems are ubiquitous in real-world recommendation systems, such as news recommendation, but suffer from data sparsity and lack of negative items. To address the challenge, the state-of-the-art algorithm assigns uninteracted items with smaller weights of being negative and performs low-rank approximation over the user-item interaction matrix. However, the prior ratings are usually suggested to be zero but may not be well-defined. To avert the direct utilization of prior ratings for uninteracted items, we propose a novel ranking-based implicit regularizer by hypothesizing that users’ preference scores for uninteracted items should not deviate a lot from each other. The regularizer is then used in a ranking-based OCCF framework to penalize large differences of preference scores between uninteracted items. To efficiently optimize model parameters in this framework, we develop the scalable alternating least square algorithm and coordinate descent algorithm, whose time complexity is linearly proportional to the data size. Finally, we extensively evaluate the proposed algorithms on six public real-world datasets. The results show that the proposed regularizer significantly improves the recommendation quality of ranking-based OCCF algorithms, such as BPRMF and RankALS. Moreover, the ranking-based framework with the proposed regularizer outperforms the state-of-the-art recommendation algorithms for implicit feedback.

The implicit feedback based collaborative filtering (CF) has attracted much attention in recent years, mainly because users implicitly express their preferences in many real-world scenarios. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proved to be helpful to exploit binary relevance data, but lead to either not address the ranking problem, or not specifically focus on top-$k$recommendation. Although there exists the listwise method maximizes the Mean Reciprocal Rank (MRR), it has low efficiency and is not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namelyCollaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we smooth another well-known rank-biased measure called Mean Average Precision (MAP), and respectively combine two rank-biased metrics (MAP, MRR) with the pairwise objective function to capture the performance of top-$k$recommendation. Furthermore, the sampling scheme for CLAPF is discussed to accelerate the convergence speed. Our CLAPF framework is a new hybrid model that provides an idea of utilizing rank-biased measures in a pairwise way on implicit feedback. Empirical studies demonstrated CLAPF outperforms state-of-the-art approaches on real-world datasets.

Social recommendation has achieved great success in many domains including e-commerce and location-based social networks. Existing methods usually explore the user-item interactions or user-user connections to predict users’ preference behaviors. However, they usually learn both user and item representations in Euclidean space, which has large limitations for exploring the latent hierarchical property in the data. In this article, we study a novel problem of hyperbolic social recommendation, where we aim to learn the compact but strong representations for both users and items. Meanwhile, this work also addresses two critical domain-issues, which are under-explored. First, users often make trade-offs with multiple underlying aspect factors to make decisions during their interactions with items. Second, users generally build connections with others in terms of different aspects, which produces different influences with aspects in social network. To this end, we propose a novel graph neural network (GNN) framework with multiple aspect learning, namely, HyperSoRec. Specifically, we first embed all users, items, and aspects into hyperbolic space with superior representations to ensure their hierarchical properties. Then, we adapt a GNN with novel multi-aspect message-passing-receiving mechanism to capture different influences among users. Next, to characterize the multi-aspect interactions of users on items, we propose an adaptive hyperbolic metric learning method by introducing learnable interactive relations among different aspects. Finally, we utilize the hyperbolic translational distance to measure the plausibility in each user-item pair for recommendation. Experimental results on two public datasets clearly demonstrate that our HyperSoRec not only achieves significant improvement for recommendation performance but also shows better representation ability in hyperbolic space with strong robustness and reliability.

Person search has long been treated as a crucial and challenging task to support deeper insight in personalized summarization and personality discovery. Traditional methods, e.g., person re-identification and face recognition techniques, which profile video characters based on visual information, are often limited by relatively fixed poses or small variation of viewpoints and suffer from more realistic scenes with high motion complexity (e.g., movies). At the same time, long videos such as movies often have logical story lines and are composed of continuously developmental plots. In this situation, different persons usually meet on a specific occasion, in which informative social cues are performed. We notice that these social cues could semantically profile their personality and benefit person search task in two aspects. First, persons with certain relationships usually co-occur in short intervals; in case one of them is easier to be identified, the social relation cues extracted from their co-occurrences could further benefit the identification for the harder ones. Second, social relations could reveal the association between certain scenes and characters (e.g., classmate relationship may only exist among students), which could narrow down candidates into certain persons with a specific relationship. In this way, high-level social relation cues could improve the effectiveness of person search. Along this line, in this article, we propose a social context-aware framework, which fuses visual and social contexts to profile persons in more semantic perspectives and better deal with person search task in complex scenarios. Specifically, we first segment videos into several independent scene units and abstract out social contexts within these scene units. Then, we construct inner-personal links through a graph formulation operation for each scene unit, in which both visual cues and relation cues are considered. Finally, we perform a relation-aware label propagation to identify characters’ occurrences, combining low-level semantic cues (i.e., visual cues) and high-level semantic cues (i.e., relation cues) to further enhance the accuracy. Experiments on real-world datasets validate that our solution outperforms several competitive baselines.

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.

Stock trend prediction plays a crucial role in quantitative investing. Given the prediction task on a certain granularity (e.g., daily trend), a large portion of existing studies merely leverage market data of the same granularity (e.g., daily market data). In financial investment scenarios, however, there exist amounts of finer-grained information (e.g., high-frequency data) that contain more detailed investment signals beyond the original granularity data. This motivates us to investigate how to leverage multi-granularity market data to enhance the accuracy of stock trend prediction. Some straightforward methods, such as concatenating finer-grained data as features or fusing with a model based on finer-grained features, may not lead to more precise stock trend prediction due to some unique challenges. First, the inconsistency of granularity between the target trend and finer-grained data could substantially increase optimization difficulty, such as the relative sparsity of the target trend compared with higher dimensions of finer-grained features. Moreover, the continuously changing financial market state could result in varying efficacy of heterogeneous multi-granularity information, which consequently requires a dynamic approach for proper fusion among them. In this paper, we propose the Contrastive Multi-Granularity Learning Framework (CMLF) to address these challenges. Particularly, we first design two novel contrastive learning objectives at the pre-training stage to address the inconsistency issue by constructing additional self-supervised signals relying on the inherent character of stock data. We also design a gate mechanism based on market-aware technical indicators to fuse the multi-granularity features at each time step adaptively. Extensive experiments on three real-world datasets show significant improvements of our approach over the state-of-the-art baselines on stock trend prediction and profitability in real investing scenarios.

Recent studies in recommender systems have managed to achieve significantly improved performance. However, despite being extensively studied, these methods still suffer from two limitations. First, previous studies either encode the document or extract latent sentiment via neural networks, which are difficult to interpret the sentiment of reviewers intuitively. Second, they neglect the personalized interaction of reviews with user/item, i.e., each review has different contributions when modeling the preference of user/item

Learning effective user representations from sequential user-item interactions is a fundamental problem for recommender systems (RS). Recently, several unsupervised methods focusing on user representations pre-training have been explored. In general, these methods apply similar learning paradigms by first corrupting the behavior sequence, and then restoring the original input with some item-level prediction loss functions. Despite its effectiveness, we argue that there exist important gaps between such item-level optimization objective and user-level representations, and as a result, the learned user representations may only lead to sub-optimal generalization performance. In this paper, we propose a novel self-supervised pre-training framework, called CLUE, which stands for employing Contrastive Learning for modeling sequence-level User rEpresentation. The core idea of CLUE is to regard each user behavior sequence as a whole and then construct the self-supervision signals by transforming the original user behaviors by data augmentations (DA). Specifically, we employ two Siamese (weight-sharing) networks to learn the user-oriented representations, where the optimization goal is to maximize the similarity of learned representations of the same user by these two encoders. More importantly, we perform careful investigation of the impacts of view generating strategies for user behavior inputs from a more comprehensive perspective, including processing sequential behaviors by explicit DA strategies and employing dropout as implicit DA. To verify the effectiveness of CLUE, we perform extensive experiments on several user-related tasks with different scales and characteristics. Our experimental results show that the user representations learned by CLUE surpass existing item-level baselines under several evaluation protocols.

In intelligent education systems, question difficulty prediction (QDP) is a fundamental task of many applications, such as personalized question recommendation and test paper analysis. Previous work mainly focus on data-driven QDP methods, which are heavily relied on the large-scale labeled dataset of courses. To alleviate the labor intensity, an intuitive method is to introduce domain adaptation into QDP and consider each course as a domain. In educational psychology, there are two factors influencing difficulty common to different courses: the obstacles of comprehending the question and generating a response, namely stimulus and task difficulty. To this end, we propose a novel Stimulus and Task difficulty-based Adversarial Network (STAN) that models question difficulty from the views of stimulus and task. Then, in order to align the difficulty distribution of the source domain and the target domain, we utilize the conditional adversarial learning with readability-enhanced pseudo-labels. Meanwhile, we proposed a sampling method based on density estimation to implicit alignment. Finally, we conduct experiments on the real questions datasets to evaluate the effectiveness of our QDP model and domain adaptation method. Our method significantly improves accuracy over state-of-the-art methods on real-world question data of multiple courses.

Most cognitive diagnosis research in education has been concentrated on individual assessment, aiming at discovering the latent characteristics of students. However, in many real-world scenarios, group-level assessment is an important and meaningful task, e.g., class assessment in different regions can discover the difference of teaching level in different contexts. In this work, we consider assessing cognitive ability for a group of students, which aims to mine groups’ proficiency on specific knowledge concepts. The significant challenge in this task is the sparsity of group-exercise response data, which seriously affects the assessment performance. Existing works either do not make effective use of additional student-exercise response data or fail to reasonably model the relationship between group ability and individual ability in different learning contexts, resulting in sub-optimal diagnosis results. To this end, we propose a general Multi-Task based Group-Level Cognitive Diagnosis (MGCD) framework, which is featured with three special designs: 1) We jointly model student-exercise responses and group-exercise responses in a multi-task manner to alleviate the sparsity of group-exercise responses; 2) We design a context-aware attention network to model the relationship between student knowledge state and group knowledge state in different contexts; 3) We model an interpretable cognitive layer to obtain student ability, group ability and exercise factors (e.g., difficulty), and then we leverage neural networks to learn complex interaction functions among them. Extensive experiments on real-world datasets demonstrate the generality of MGCD and the effectiveness of our attention design and multi-task learning.

With the accelerated technology development, technological trend forecasting through patent mining has become a hot issue for high-tech companies. In this term, extensive attention has been attracted to forecasting technological knowledge flows (TKF), i.e., predicting the directional flows of knowledge from one technological field to another. However, existing studies either rely on labor intensive empirical analysis or do not consider the intrinsic characteristics inherent in TKF, including the double-faced aspects (i.e., act as both the source and target) of technology nodes, multiple complex relationships among different technologies, and dynamics of the TKF process. To this end, in this paper, we make a further study and propose a data-driven solution, i.e., a Hierarchical Interactive Graph Neural Network (HighTKF), to automatically find the potential flow trends of technologies. Specifically, HighTKF makes final predictions through two kinds of representations of each technology node (a diffusion vector and an absorption vector), which is realized by three components: High-Order Interaction Module (HOI), Hierarchical Delivery Module (HD) and Technology Flow Tracing Module (TFT). For one thing, HOI and HD aim to model high-order network relationships and hierarchical relationships among technologies. For another, TFT is designed for capturing the dynamic feature evolution of technologies with the above relations involved. Also, we design a hybrid loss function and propose a new evaluation metric for better predicting the unprecedented flows between technologies. Finally, we conduct extensive experiments on a real-world patent dataset, the results verify the effectiveness of our approach and reveal some interesting phenomenons on technological knowledge flow trends.

Assessing the proficiency of trial lawyers in different legal fields is of significant importance since a qualified lawyer or lawyer team can strive for his clients' best rights while ensuring the fairness of litigations. However, proficiency assessment for lawyers is very challenging due to many technical and domain challenges, such as the lack of unified evaluation standards, and the complex interactions between lawyers and cases in real legal systems. To this end, we propose a novel proficiency assessment network for trial lawyers (LawyerPAN) to quantify lawyer proficiency through online litigation records. Specifically, we first leverage the theories in psychological measurement for mapping the proficiency of lawyers in each field into a unified real number space. Meanwhile, the characteristics of cases (i.e., case difficulty and discrimination) are well modeled to ensure fairness when assessing lawyers in different cases and fields. Then, we model the interactions between lawyers and cases from two perspectives: the anticipatory perspective aims to measure the personal proficiency of anticipated strategy, and the adversarial perspective seeks to depict the gap of lawyers' proficiency between both sides (i.e., plaintiffs and defendants). Finally, we conduct extensive experiments on real-world data, and the results show the effectiveness and interpretability of our approaches on assessing the proficiency of trial lawyers.

Learning informative representations for educational questions is a fundamental problem in online learning systems, which can promote many applications, e.g., difficulty estimation. Most solutions integrate all information of one question together following a supervised manner, where the representation results are unsatisfactory sometimes due to the following issues. First, they cannot ensure the presentation ability due to the scarcity of labeled data. Then, the label-dependent representation results have poor feasibility to be transferred. Moreover, aggregating all information into the unified may introduce some noises in applications since it cannot distinguish the diverse characteristics of questions. In this paper, we aim to learn the disentangled representations of questions. We propose a novel unsupervised model, namely DisenQNet, to divide one question into two parts, i.e., a concept representation that captures its explicit concept meaning and an individual representation that preserves its personal characteristics. We achieve this goal via mutual information estimation by proposing three self-supervised estimators in a large unlabeled question corpus. Then, we propose another enhanced model, DisenQNet+, that transfers the representation knowledge from unlabeled questions to labeled questions in specific applications by maximizing the mutual information between both. Extensive experiments on real-world datasets demonstrate that DisenQNet can generate effective and meaningful disentangled representations for questions, and furthermore, DisenQNet+ can improve the performance of different applications.

Approximate nearest neighbor search (ANNs) plays an important role in many applications ranging from information retrieval, recommender systems to machine translation. Several ANN indexes, such as hashing and quantization, have been designed to update for the evolving database, but there exists a remarkable performance gap between them and retrained indexes on the entire database. To close the gap, we propose an online additive quantization algorithm (online AQ) to dynamically update quantization codebooks with the incoming streaming data. Then we derive the regret bound to theoretically guarantee the performance of the online AQ algorithm. Moreover, to improve the learning efficiency, we develop a randomized block beam search algorithm for assigning each data to the codewords of the codebook. Finally, we extensively evaluate the proposed online AQ algorithm on four real-world datasets, showing that it remarkably outperforms the state-of-the-art baselines.

Knowledge tracing (KT), which aims to trace students' changing knowledge state during their learning process, has improved students' learning efficiency in online learning systems. Recently, KT has attracted much research attention due to its critical significance in education. However, most of the existing KT methods pursue high accuracy of student performance prediction but neglect the consistency of students' changing knowledge state with their learning process. In this paper, we explore a new paradigm for the KT task and propose a novel model named Learning Process-consistent Knowledge Tracing (LPKT), which monitors students' knowledge state through directly modeling their learning process. Specifically, we first formalize the basic learning cell as the tuple exercise---answer time---answer. Then, we deeply measure the learning gain as well as its diversity from the difference of the present and previous learning cells, their interval time, and students' related knowledge state. We also design a learning gate to distinguish students' absorptive capacity of knowledge. Besides, we design a forgetting gate to model the decline of students' knowledge over time, which is based on their previous knowledge state, present learning gains, and the interval time. Extensive experimental results on three public datasets demonstrate that LPKT could obtain more reasonable knowledge state in line with the learning process. Moreover, LPKT also outperforms state-of-the-art KT methods on student performance prediction. Our work indicates a potential future research direction for KT, which is of both high interpretability and accuracy.

As an important means to solve information overload, recommender systems have been widely applied in many fields, such as e-commerce and advertising. However, recent studies have shown that recommender systems are vulnerable to poisoning attacks; that is, injecting a group of carefully designed user profiles into the recommender system can severely affect recommendation quality. Despite the development from shilling attacks to optimization-based attacks, the imperceptibility and harmfulness of the generated data in most attacks are arduous to balance. To this end, we propose a triple adversarial learning for influence based poisoning attack (TrialAttack), a flexible end-to-end poisoning framework to generate non-notable and harmful user profiles. Specifically, given the input noise, TrialAttack directly generates malicious users through triple adversarial learning of the generator, discriminator, and influence module. Besides, to provide reliable influence for TrialAttack training, we explore a new approximation approach for estimating each fake user's influence. Through theoretical analysis, we prove that the distribution characterized by TrialAttack approximates to the rating distribution of real users under the premise of performing an efficient attack. This property allows the injected users to attack in an unremarkable way. Experiments on three real-world datasets show that TrialAttack's attack performance outperforms state-of-the-art attacks, and the generated fake profiles are more difficult to detect compared to baselines.

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.

Urban crowd density prediction, which predicts the future crowd density in different areas based on the historical data, is playing an increasingly significant role in epidemic prevention and traffic optimization. Most existing methods model the spatial information through a single relationship, i.e., distance, and extract the temporal information only by short time sequences, which limits the model to fully capture the spatiotemporal information. Therefore, in this paper, we propose a Multi-relational Graph Convolutional Gate Recurrent Unit (MGC-GRU) model to represent the spatiotemporal information more comprehensively for better urban crowd density prediction. Specifically, we first construct a multi-relation urban area graph to enrich the spatial relationship between areas. Then a graph representation module based on a multi-relational graph convolution network is proposed to represent spatial information of the area, in which aggregator distinguishes the information of different relationships and propagator equips the self-attention mechanism to refine the representation. Afterwards, we further construct a fine-grained sequence prediction module to enhance the temporal dependency by modeling time sequences in different granularity, i.e., daily and hourly. Finally, extensive experiments on a real-world dataset demonstrate the superior performance of MGC-GRU on urban crowd density prediction task.

One-hot encoder accompanied by a softmax loss has become the default configuration to deal with the multiclass problem, and is also prevalent in deep learning (DL) based recommender systems (RS). The standard learning process of such methods is to fit the model outputs to a one-hot encoding of the ground truth, referred to as the hard target. However, it is known that these hard targets largely ignore the ambiguity of unobserved feedback in RS, and thus may lead to sub-optimal generalization performance. In this work, we propose SoftRec, a new RS optimization framework to enhance item recommendation. The core idea is that we add additional supervisory signals - well-designed soft targets - for each instance so as to better guide the recommender learning. Meanwhile, we carefully investigate the impacts of specific soft target distributions by instantiating the SoftRec with a series of strategies, including item-based, user-based, and model-based. To verify the effectiveness of SoftRec, we conduct extensive experiments on two public recommendation datasets by using various deep recommendation architectures. The experimental results show that our methods achieve superior performance compared with the standard optimization approaches. Moreover, SoftRec could also exhibit strong performance in cold-start scenarios where user-item interaction has higher sparsity.

The factorization-based models have achieved great success in online advertisements and recommender systems due to the capability of efficiently modeling combinational features. These models encode feature interactions by the vector product between feature embedding. Despite the improvement of generalization, the memory consumption of these models grows significantly, because they usually take hundreds to thousands of large categorical features as input. Several existing works try to reduce the memory footprint by hashing, randomized embedding composition, and dimensionality search, but they suffer from either substantial performance degradation or limited memory compression. To this end, in this paper, we propose an extremely memory-efficient Factorization Machine (xLightFM), where each category embedding is composited with latent vectors selected from codebooks. Based on the characteristics of each categorical feature, we further propose to adapt the codebook size with the neural architecture search techniques for compositing the embedding of each categorical feature. This further pushes the limits of memory compression while incurring negligible degradation or even some improvements in prediction performance. We extensively evaluate the proposed algorithm with two real-world datasets. The results demonstrate that xLightFM can outperform the state-of-the-art lightweight factorization-based methods in terms of both prediction quality and memory footprint, and achieve more than 18x and 27x memory compression compared to the vanilla FM on these two datasets, respectively.

Representation learning of examination papers is the cornerstone of the Examination Paper Analysis (EPA) in education area including Paper Difficulty Prediction (PDR) and Finding Similar Papers (FSP). Previous works mainly focus on the representation learning of each test item, but few works notice the hierarchical document structure in examination papers. To this end, in this paper, we propose a novel Examination Organization Encoder (EOE) to learn a robust representation of the examination paper with the hierarchical document structure. Specifically, we first propose a syntax parser to recover the hierarchical document structure and convert an examination paper to an Examination Organization Tree (EOT), where the test items are the leaf nodes and the internal nodes are summarization of their child nodes. Then, we applied a two-layer GRU-based module to obtain the representation of each leaf node. After that, we design a subtree encoder module to aggregate the representation of each leaf node, which is used to calculate an embedding for each layer in the EOT. Finally, we feed all the layer embedding into an output module, the process is over and we get the examination paper representation that can be used for downstream tasks. Extensive experiments on real-world data demonstrate the effectiveness and interpretability of our method.

Recent studies have shown that recommender systems are vulnerable, and it is easy for attackers to inject well-designed malicious profiles into the system, leading to biased recommendations. We cannot deny these data's rationality, making it imperative to establish a robust recommender system. Adversarial training has been extensively studied for robust recommendations. However, traditional adversarial training adds small perturbations to the parameters (inputs), which do not comply with the poisoning mechanism in the recommender system. Thus for the practical models that are very good at learning existing data, it does not perform well. To address the above limitations, we propose adversarial poisoning training (APT). It simulates the poisoning process by injecting fake users (ERM users) who are dedicated to minimizing empirical risk to build a robust system. Besides, to generate ERM users, we explore an approximation approach to estimate each fake user's influence on the empirical risk. Although the strategy of "fighting fire with fire" seems counterintuitive, we theoretically prove that the proposed APT can boost the upper bound of poisoning robustness. Also, we deliver the first theoretical proof that adversarial training holds a positive effect on enhancing recommendation robustness. Through extensive experiments with five poisoning attacks on four real-world datasets, the results show that the robustness improvement of APT significantly outperforms baselines. It is worth mentioning that APT also improves model generalization in most cases.

Knowledge tracing is a fundamental task in intelligent education for tracking the knowledge states of students on necessary concepts. In recent years, Deep Knowledge Tracing (DKT) utilizes recurrent neural networks to model student learning sequences. This approach has achieved significant success and has been widely used in many educational applications. However, in practical scenarios, it tends to suffer from the following critical problems due to data isolation: 1) Data scarcity. Educational data, which is usually distributed across different silos (e.g., schools), is difficult to gather. 2) Different data quality. Students in different silos have different learning schedules, which results in unbalanced learning records, meaning that it is necessary to evaluate the learning data quality independently for different silos. 3) Data incomparability. It is difficult to compare the knowledge states of students with different learning processes from different silos. Inspired by federated learning, in this paper, we propose a novel Federated Deep Knowledge Tracing (FDKT) framework to collectively train high-quality DKT models for multiple silos. In this framework, each client takes charge of training a distributed DKT model and evaluating data quality by leveraging its own local data, while a center server is responsible for aggregating models and updating the parameters for all the clients. In particular, in the client part, we evaluate data quality incorporating different education measurement theories, and we construct two quality-oriented implementations based on FDKT, i.e., FDKTCTT and FDKTIRT-where the means of data quality evaluation follow Classical Test Theory and Item Response Theory, respectively. Moreover, in the server part, we adopt hierarchical model interpolation to uptake local effects for model personalization. Extensive experiments on real-world datasets demonstrate the effectiveness and superiority of the FDKT framework.

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being extensively studied, these sequential methods still suffer from three limitations. First, existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction, because users often click on new products that are irrelevant to any historical behaviors. Second, in the real scenario, there are numerous users that have operations a long time ago, but turn relatively inactive in recent times. Thus, it is hard to precisely capture user's current preferences through early behaviors. Third, multiple representations of user's historical behaviors in different feature subspaces are largely ignored. To remedy these issues, we propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features (e.g., gender, age and occupation in user-profile). Specifically, MIAN contains a Multi-Interactive Layer (MIL) that integrates three local interaction modules to capture multiple representations of user preference through sequential behaviors and simultaneously utilize the fine-grained user-specific as well as context information. In addition, we design a Global Interaction Module (GIM) to learn the high-order interactions and balance the different impacts of multiple features. Finally, Offline experiment results from three datasets, together with an Online A/B test in a large-scale recommendation system, demonstrate the effectiveness of our proposed approach.

User modeling aims to capture the latent characteristics of users from their behaviors, and is widely applied in numerous applications. Usually, centralized user modeling suffers from the risk of privacy leakage. Instead, federated user modeling expects to provide a secure multi-client collaboration for user modeling through federated learning. Existing federated learning methods are mainly designed for consistent clients, which cannot be directly applied to practical scenarios, where different clients usually store inconsistent user data. Therefore, it is a crucial demand to design an appropriate federated solution that can better adapt to user modeling tasks, and however, meets following critical challenges: 1) Statistical heterogeneity. The distributions of user data in different clients are not always independently identically distributed which leads to personalized clients; 2) Privacy heterogeneity. User data contains both public and private information, which have different levels of privacy. It means we should balance different information to be shared and protected; 3) Model heterogeneity. The local user models trained with client records are heterogeneous which need flexible aggregation in the server. In this paper, we propose a novel client-server architecture framework, namely Hierarchical Personalized Federated Learning (HPFL) to serve federated learning in user modeling with inconsistent clients. In the framework, we first define hierarchical information to finely partition the data with privacy heterogeneity. On this basis, the client trains a user model which contains different components designed for hierarchical information. Moreover, client processes a fine-grained personalized update strategy to update personalized user model for statistical heterogeneity. Correspondingly, the server completes a differentiated component aggregation strategy to flexibly aggregate heterogeneous user models in the case of privacy and model heterogeneity. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of the HPFL framework.

Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation. However, prior arts mainly follow the traditional recommendation strategies like collaborative filtering, which usually treat individual drugs as mutually independent, while the latent interactions among drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect within drugs, in which the interaction effects could be affected by patient conditions. Specifically, we first design a pre-training method based on neural collaborative filtering to get the initial embedding of patients and drugs. Then, the drug interaction graph will be initialized based on medical records and domain knowledge. Along this line, we propose a new Drug Package Recommendation (DPR) framework with two variants, respectively DPR on Weighted Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in which each the interactions will be described as signed weights or attribute vectors. In detail, a mask layer is utilized to capture the impact of patient condition, and graph neural networks (GNNs) are leveraged for the final graph induction task to embed the package. Extensive experiments on a real-world data set from a first-rate hospital demonstrate the effectiveness of our DPR framework compared with several competitive baseline methods, and further support the heuristic study for the drug package generation task with adequate performance.

Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically modeled; (2) most existing algorithms place attention mechanisms on top of recurrent network, so they can not allow for full parallelism and are inferior to self-attention for capturing long-range dependence; (3) most literature does not make good use of long-term historical information and do not effectively model the long-term periodicity of users. To this end, we propose MoveNet and RLMoveNet. MoveNet is a self-attention-based sequential model, predicting each user’s next destination based on her most recent visits and historical trajectory. MoveNet first introduces a cross-based learning framework for modeling feature interactions. With self-attention on both the most recent visits and historical trajectory, MoveNet can use an attention mechanism to capture the user’s long-term regularity in a more efficient way. Based on MoveNet, to model long-term periodicity more effectively, we add the reinforcement learning layer and named RLMoveNet. RLMoveNet regards the human mobility prediction as a reinforcement learning problem, using the reinforcement learning layer as the regularization part to drive the model to pay attention to the behavior with periodic actions, which can help us make the algorithm more effective. We evaluate both of them with three real-world mobility datasets. MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup. Moreover, RLMoveNet achieves higher prediction accuracy than MoveNet, which proves that modeling periodicity explicitly from the perspective of reinforcement learning is more effective.

Binary representation of users and items can dramatically improve efficiency of recommendation and reduce size of recommendation models. However, learning optimal binary codes for them is challenging due to binary constraints, even if squared loss is optimized. In this article, we propose a general framework for discrete matrix factorization based on discrete optimization, which can 1) optimize multiple loss functions; 2) handle both explicit and implicit feedback datasets; and 3) take auxiliary information into account without any hyperparameters. To tackle the challenging discrete optimization problem, we propose block coordinate descent based on semidefinite relaxation of binary quadratic programming. We theoretically show that it is equivalent to discrete coordinate descent when only one coordinate is in each block. We extensively evaluate the proposed algorithms on eight real-world datasets. The results of evaluation show that they outperform the state-of-the-art baselines significantly and that auxiliary information of items improves recommendation performance. For better showing the advantages of binary representation, we further propose a two-stage recommender system, consisting of an item-recalling stage and a subsequent fine-ranking stage. Its extensive evaluation shows hashing can dramatically accelerate item recommendation with little degradation of accuracy.

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.

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.

Computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee's knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Specifically, at one CAT selection step, Quality Module first quantifies the informativeness of questions and generates candidate subset with the highest quality. Then, Diversity Module selects one question at each step that maximizes the concept coverage. Additionally, we propose Importance Module to estimate the importance of concepts that optimizes the CAT selection. Under MAAT, we prove that the goal of maximizing both quality and diversity is NP-hard, but we provide efficient algorithms by exploiting the inherent submodular property. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity perspectives.

Point-of-Interest (POI) recommendation helps users find their interested places to visit based on the time and user location. Unlike traditional recommendation tasks, POI recommendation is personalized, spatial-aware, and temporally dependent. Although many previous works have tried modeling spatial and temporal characteristics, most of them suffer from the following two limitations: For the spatial aspect, existing works only consider the user-POI distance or POI-POI distance. However, we find that a user prefers different regions at different times, which is known as user-region periodic pattern. For the temporal aspect, most works treat user and time as two independent factors. However, different users may prefer the same POI in different time periods, which is known as user-POI periodic pattern. To address the limitation of existing works, we propose a novel Spatial-Temporal aware Graph Convolutional Neural Network (STGCN) for POI recommendation. Specifically, we first design a user record multigraph to fuse all the context information into a unified graph. Then, we propose a time-based neighborhood sampling algorithm and take advantage of the flexible propagation mechanism of GCNs to learn the representations of each node at a specific time. Furthermore, multiple scoring functions are proposed to exploit user-region periodic pattern and user-POI periodic pattern, respectively. We also develop a time smoothing strategy to alleviate the data sparsity problem. Extensive experiments are conducted on two real-world datasets, and the experimental results demonstrate the effectiveness of our method.

Recent years have witnessed a booming increase of patent applications, which provides an open chance for revealing the inner law of innovation, but in the meantime, puts forward higher requirements on patent mining techniques. Considering that patent mining highly relies on patent document analysis, this paper makes a focused study on constructing a technology portrait for each patent, i.e., to recognize technical phrases concerned in it, which can summarize and represent patents from a technology angle. To this end, we first give a clear and detailed description about technical phrases in patents based on various prior works and analyses. Then, combining characteristics of technical phrases and multi-level structures of patent documents, we develop an Unsupervised Multi-level Technical Phrase Extraction (UMTPE) model. Particularly, a novel evaluation metric called Information Retrieval Efficiency (IRE) is designed to evaluate the extracted phrases from a new perspective, which greatly supplements traditional metrics like Precision and Recall. Finally, extensive experiments on real-world patent data show the effectiveness of our UMTPE model.

Knowledge Tracing (KT) is a fundamental but challenging task in online education that traces learners' evolving knowledge states. Much attention has been drawn to this area and several works such as Bayesian Knowledge Tracing and Deep Knowledge Tracing are proposed. Recent works have explored the value of relations among concepts and proposed to introduce knowledge structure into KT task. However, the propagated influence among concepts, which has been shown to be a key factor in human learning by the educational theories, is still under-explored. In this paper, we propose a new framework called Structure-based Knowledge Tracing (SKT), which exploits the multiple relations in knowledge structure to model the influence propagation among concepts. In the SKT framework, we not only consider the temporal effect on the exercising sequence but also take the spatial effect on the knowledge structure into account. We take advantages of two novel formulations in modeling the influence propagation on the knowledge structure with multiple relations. For undirected relations such as similarity relations, the synchronization propagation method is adopted, where the influence propagates bidirectionally between neighbor concepts. For directed relations such as prerequisite relations, the partial propagation method is applied, where the influence can only unidirectionally propagate from a predecessor to a successor. Meanwhile, we employ the gated functions to update the states of concepts temporally and spatially. Extensive experiments demonstrate the effectiveness and interpretability of SKT.

In education systems, Finding Similar Exercises (FSE) is the key step for both exercise retrieval and duplicate detection. Recently, more and more attention has been drawn into this area and several works have been proposed, to utilize the exercise content (e.g., texts or images) or the labeled knowledge concepts. Such approaches, however, have failed to take knowledge hierarchy into account. To this end, we advance a novel knowledge-aware multimodal network, namely KnowNet, for finding similar exercises in large-scale online education systems by integrating the knowledge hierarchy into the heterogeneous exercise data and learning a relation-aware semantic representation. Specifically, we first propose a Content Representation Layer (CRL) to learn a unified semantic representation of the heterogeneous exercise content. Then, we design a Hierarchy Fusion Layer (HFL) to exploit the knowledge hierarchy. By combining the knowledge hierarchy, HFL can not only retrieve the relation-aware semantic representation but also provide an interpretable view to investigate the similarity of exercises. Finally, we adopt a Similarity Score Layer (SSL) for returning similar exercises. Extensive experiments demonstrate the effectiveness and interpretability of KnowNet.

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.

Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of scarce labeled molecules in the chemical space, where such property labels are generally obtained by Density Functional Theory (DFT) calculation which is extremely computational costly. An effective solution is to incorporate the unlabeled molecules in a semi-supervised fashion. However, learning semi-supervised representation for large amounts of molecules is challenging, including the joint representation issue of both molecular essence and structure, the conflict between representation and property leaning. Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules. Specifically, ASGN adopts a teacher-student framework. In the teacher model, we propose a novel semi-supervised learning method to learn general representation that jointly exploits information from molecular structure and molecular distribution. Then in the student model, we target at property prediction task to deal with the learning loss conflict. At last, we proposed a novel active learning strategy in terms of molecular diversities to select informative data during the whole framework learning. We conduct extensive experiments on several public datasets. Experimental results show the remarkable performance of our ASGN framework.

Sequential location recommendation plays an important role in many applications such as mobility prediction, route planning and location-based advertisements. In spite of evolving from tensor factorization to RNN-based neural networks, existing methods did not make effective use of geographical information and suffered from the sparsity issue. To this end, we propose a Geography-aware sequential recommender based on the Self-Attention Network (GeoSAN for short) for location recommendation. On the one hand, we propose a new loss function based on importance sampling for optimization, to address the sparsity issue by emphasizing the use of informative negative samples. On the other hand, to make better use of geographical information, GeoSAN represents the hierarchical gridding of each GPS point with a self-attention based geography encoder. Moreover, we put forward geography-aware negative samplers to promote the informativeness of negative samples. We evaluate the proposed algorithm with three real-world LBSN datasets, and show that GeoSAN outperforms the state-of-the-art sequential location recommenders by 34.9%. The experimental results further verify significant effectiveness of the new loss function, geography encoder, and geography-aware negative samplers.

Automatically answering mathematical problems is a challenging task since it requires not only the ability of linguistic understanding but also mathematical comprehension. Existing studies usually explore solutions on the elementary math word problems that aim to understand the questions described in natural language narratives, which are not capable of solving more general problems containing structural formulas. To this end, in this paper, we propose a novel Neural Mathematical Solver (NMS) with enhanced formula structures. Specifically, we first frame the formulas in a certain problem as a TeX dependency graph to preserve formula-enriched structures. Then, we design a formula graph network (FGN) to capture its mathematical relations. Next, we develop a novel architecture with two GRU models, connecting tokens from both word space and formula space together, to learn the linguistic semantics for the answers. Extensive experiments on a large-scale dataset demonstrate that NMS not only achieves better answer prediction but also visualizes reasonable mathematical representations of problems.

With the development of online education systems, a growing number of research works are focusing on Knowledge Tracing (KT), which aims to assess students' changing knowledge state and help them learn knowledge concepts more efficiently. However, only given student learning interactions, most of existing KT methods neglect the individualization of students, i.e., the prior knowledge and learning rates differ from student to student. To this end, in this paper, we propose a novel Convolutional Knowledge Tracing (CKT) method to model individualization in KT. Specifically, for individualized prior knowledge, we measure it from students' historical learning interactions. For individualized learning rates, we design hierarchical convolutional layers to extract them based on continuous learning interactions of students. Extensive experiments demonstrate that CKT could obtain better knowledge tracing results through modeling individualization in learning process. Moreover, CKT can learn meaningful exercise embeddings automatically.

As the task of predicting a personalized ranking on a set of items, item recommendation has become an important way to address information overload. Optimizing ranking loss aligns better with the ultimate goal of item recommendation, so many ranking-based methods were proposed for item recommendation, such as collaborative filtering with Bayesian Personalized Ranking (BPR) loss, and Weighted Approximate-Rank Pairwise (WARP) loss. However, the ranking-based methods can not consistently beat regression-based models with the gravity regularizer. The key challenge in ranking-based optimization is difficult to fully use the limited number of negative samples, particularly when they are not so informative. To this end, we propose a new ranking loss based on importance sampling so that more informative negative samples can be better used. We then design a series of negative samplers from simple to complex, whose informativeness of negative samples is from less to more. With these samplers, the loss function is easy to use and can be optimized by popular solvers. The proposed algorithms are evaluated with five real-world datasets of varying size and difficulty. The results show that they consistently outperform the state-of-the-art item recommendation algorithms, and the relative improvements with respect to [email protected] are more than 19.2% on average. Moreover, the loss function is verified to make better use of negative samples and to require fewer negative samples when they are more informative.

Deep recommender systems have achieved remarkable improvements in recent years. Despite its superior ranking precision, the running efficiency and memory consumption turn out to be severe bottlenecks in reality. To overcome both limitations, we propose LightRec, a lightweight recommender system which enjoys fast online inference and economic memory consumption. The backbone of LightRec is a total of B codebooks, each of which is composed of W latent vectors, known as codewords. On top of such a structure, LightRec will have an item represented as additive composition of B codewords, which are optimally selected from each of the codebooks. To effectively learn the codebooks from data, we devise an end-to-end learning workflow, where challenges on the inherent differentiability and diversity are conquered by the proposed techniques. In addition, to further improve the representation quality, several distillation strategies are employed, which better preserves user-item relevance scores and relative ranking orders. LightRec is extensively evaluated with four real-world datasets, which gives rise to two empirical findings: 1) compared with those the state-of-the-art lightweight baselines, LightRec achieves over 11% relative improvements in terms of recall performance; 2) compared to conventional recommendation algorithms, LightRec merely incurs negligible accuracy degradation while leads to more than 27x speedup in top-k recommendation.

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.

Music plays an important role in our daily life. With the development of deep learning and modern generation techniques, researchers have done plenty of works on automatic music generation. However, due to the special requirements of both melody and arrangement, most of these methods have limitations when applying to multi-track music generation. Some critical factors related to the quality of music are not well addressed, such as chord progression, rhythm pattern, and musical style. In order to tackle the problems and ensure the harmony of multi-track music, in this article, we propose an end-to-end melody and arrangement generation framework to generate a melody track with several accompany tracks played by some different instruments. To be specific, we first develop a novel Chord based Rhythm and Melody Cross-Generation Model to generate melody with a chord progression. Then, we propose a Multi-Instrument Co-Arrangement Model based on multi-task learning for multi-track music arrangement. Furthermore, to control the musical style of arrangement, we design a Multi-Style Multi-Instrument Co-Arrangement Model to learn the musical style with adversarial training. Therefore, we can not only maintain the harmony of the generated music but also control the musical style for better utilization. Extensive experiments on a real-world dataset demonstrate the superiority and effectiveness of our proposed models.

Online donation-based crowdfunding has brought new life to charity by soliciting small monetary contributions from crowd donors to help others in trouble or with dreams. However, a crucial issue for crowdfunding platforms as well as traditional charities is the problem of high donor attrition, i.e., many donors donate only once or very few times within a rather short lifecycle and then leave. Thus, it is an urgent task to analyze the factors of and then further predict the donors behaviors. Especially, we focus on two types of behavioral events, e.g., donation recurrence (whether one donor will make donations at some time slices in the future) and donor retention (whether she will remain on the crowdfunding platform until a future time). However, this problem has not been well explored due to many domain and technical challenges, such as the heterogeneous influence, the relevance of the two types of events, and the censoring phenomenon of retention records. In this paper, we present a focused study on donation recurrence and donor retention with the help of large-scale behavioral data collected from crowdfunding. Specifically, we propose a Joint Deep Survival model, i.e., JDS, which can integrate heterogeneous features, e.g., donor motives, projects recently donated to, social contacts, to jointly model the donation recurrence and donor retention since these two types of behavioral events are highly relevant. In addition, we model the censoring phenomenon and dependence relations of different behaviors from the survival analysis view by designing multiple innovative constraints and incorporating them into the objective functions. Finally, we conduct extensive analysis and validation experiments with large-scale data collected from Kiva.org. The experimental results clearly demonstrate the effectiveness of our proposed models for analyzing and predicting the donation recurrence and donor retention in crowdfunding.

The rapid development of the technologies for online learning provides students with extensive resources for self-learning and brings new opportunities for data-driven research on educational management. An important issue of online learning is to diagnose the knowledge proficiency (i.e., the mastery level of a certain knowledge concept) of each student. Considering that it is a common case that students inevitably learn and forget knowledge from time to time, it is necessary to track the change of their knowledge proficiency during the learning process. Existing approaches either relied on static scenarios or ignored the interpretability of diagnosis results. To address these problems, in this article, we present a focused study on diagnosing the knowledge proficiency of students, where the goal is to track and explain their evolutions simultaneously. Specifically, we first devise an explanatory probabilistic matrix factorization model, Knowledge Proficiency Tracing (KPT), by leveraging educational priors. KPT model first associates each exercise with a knowledge vector in which each element represents a specific knowledge concept with the help of Q -matrix. Correspondingly, at each time, each student can be represented as a proficiency vector in the same knowledge space. Then, our KPT model jointly applies two classical educational theories (i.e., learning curve and forgetting curve ) to capture the change of students’ proficiency level on concepts over time. Furthermore, for improving the predictive performance, we develop an improved version of KPT, named Exercise-correlated Knowledge Proficiency Tracing (EKPT), by considering the connectivity among exercises with the same knowledge concepts. Finally, we apply our KPT and EKPT models to three important diagnostic tasks, including knowledge estimation, score prediction, and diagnosis result visualization. Extensive experiments on four real-world datasets demonstrate that both of our models could track the knowledge proficiency of students effectively and interpretatively.

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.

Tracking influential users in a dynamic social network is a fundamental step in fruitful applications, such as social recommendation, network topology optimization, and blocking rumour spreading. The major obstacle in mining top influential users is that estimating users' influence spreads is \#P-hard under most influence propagation models. Previous studies along this line either seek heuristic solutions or may return meaningless results due to the lack of prior knowledge about users' influence in the dynamic network. In this paper, we tackle the problem of tracking top-k influential individuals in a dynamic social network. When a top-k query is issued, our algorithm returns a set S of more than k users. With high probability, our algorithm guarantees that S contains all real top-k influential users and there exists a relative error ε < 1$ such that the least influential user in S has influence at least $(1-ε) I^k$, where $I^k$ is the influence of the k-th most influential user and we can adjust ε via parameter settings. Controlling such a relative error enables us to obtain meaningful results even when we know nothing about the value of $I^k$ or $I^k$ changes over time in the dynamic network. In addition to the thorough theoretical results, our experimental results on large real networks clearly demonstrate the effectiveness and efficiency of our algorithm.

Cognitive diagnosis is the cornerstone of modern educational techniques. One of the most classic cognitive diagnosis methods is Item Response Theory (IRT), which provides interpretable parameters for analyzing student performance. However, traditional IRT only exploits student response results and has difficulties in fully utilizing the semantics of question texts, which significantly restricts its application. To this end, in this paper, we propose a simple yet surprisingly effective framework to enhance the semantic exploiting process, which we termed Deep Item Response Theory (DIRT). In DIRT, we first use a proficiency vector to represent student proficiency on knowledge concepts and represent question texts and knowledge concepts by dense embedding. Then, we use deep learning to enhance the process of diagnosing parameters of student and question by exploiting question texts and the relationship between question texts and knowledge concepts. Finally, with the diagnosed parameters, we adopt the item response function to predict student performance. Extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of DIRT framework.

Hierarchical multi-label text classification (HMTC) is a fundamental but challenging task of numerous applications (e.g., patent annotation), where documents are assigned to multiple categories stored in a hierarchical structure. Categories at different levels of a document tend to have dependencies. However, the majority of prior studies for the HMTC task employ classifiers to either deal with all categories simultaneously or decompose the original problem into a set of flat multi-label classification subproblems, ignoring the associations between texts and the hierarchical structure and the dependencies among different levels of the hierarchical structure. To that end, in this paper, we propose a novel framework called Hierarchical Attention-based Recurrent Neural Network (HARNN) for classifying documents into the most relevant categories level by level via integrating texts and the hierarchical category structure. Specifically, we first apply a documentation representing layer for obtaining the representation of texts and the hierarchical structure. Then, we develop an hierarchical attention-based recurrent layer to model the dependencies among different levels of the hierarchical structure in a top-down fashion. Here, a hierarchical attention strategy is proposed to capture the associations between texts and the hierarchical structure. Finally, we design a hybrid method which is capable of predicting the categories of each level while classifying all categories in the entire hierarchical structure precisely. Extensive experimental results on two real-world datasets demonstrate the effectiveness and explanatory power of HARNN.

Recommending suitable exercises to students in an online education system is highly useful. Existing approaches usually rely on machine learning techniques to mine large amounts of student interaction log data accumulated in the systems to select the most suitable exercises for each student. Generally, they mainly aim to optimize a single objective, i.e., recommending non-mastered exercises to address the immediate weakness of students. While this is a reasonable objective, there exist more beneficial multiple objectives in the long-term learning process that need to be addressed including Review & Explore, Smoothness of difficulty level and Engagement. In this paper, we propose a novel Deep Reinforcement learning framework, namely DRE, for adaptively recommending Exercises to students with optimization of above three objectives. In the framework, we propose two different Exercise Q-Networks for the agent, i.e., EQNM and EQNR, to generate recommendations following Markov property and Recurrent manner, respectively. We also propose novel reward functions to formally quantify those three objectives so that DRE could update and optimize its recommendation strategy by interactively receiving students' performance feedbacks (e.g., score). We conduct extensive experiments on two real-world datasets. Experimental results clearly show that the proposed DRE can effectively learn from the student interaction data to optimize multiple objectives in a single unified framework and adaptively recommend suitable exercises to students.

This paper introduces Alpha-Beta Sampling (ABS) strategy, which is particularly intended for the sampling problem of pairwise ranking in one-class collaborative filtering (PROCCF). Specifically, ABS strategy places more emphasis on such training examples, including positive item with a lower preference score and negative items with a higher preference score for each gradient step. Then, we provide the corresponding proofs for the ABS strategy from both gradient and ranking perspectives. First, we prove that sampled training examples by ABS strategy can update the model parameters with a large magnitude and analyze two instantiations by combining two specific pairwise algorithms. Second, it can be proved that ABS strategy is equivalent to optimizing for ranking-aware evaluation metrics like Normalized Discounted Cumulative Gain (NDCG). Furthermore, ABS strategy can be very general and applicable in a lot of pairwise structures of pairwise algorithms. Based on ABS strategy, we provide an effective sampling algorithm to dynamically draw items for each SGD update. Finally, we evaluate the ABS strategy by conducting sampling tasks in two representative pairwise algorithms. The experiment results show that the ABS strategy performs significantly better than the baseline strategies.

Concept map is an useful tool to help people organize and improve knowledge. Particularly in educational domain, it is beneficial for students and teachers to improve the learning and teaching quality. Traditionally, manual educational concept maps, provided by teachers, are quite time-consuming and limited to teachers' experience. Thus, it is meaningful to automatically construct high-quality concept maps. However, existing data-driven solutions only focus on either separate data source or single pedagogic relationship, which are not sufficient to satisfy actual demands. To this end, we propose a novel framework, named Extracting Multiple Relationships Concept Map (EMRCM), to construct multiple relations concept maps from Multi-source Data. Specifically, we design various targeted evidences to explore diverse information of multi-source data from different perspectives. Then, we employ three classic classifiers to bulid the predictive model for extracting key concepts and multiple concept relationships using the proposed evidences. We create a real dataset for empirically studying this problem. Extensive experiments on a real-world dataset show the effectiveness of our method.

Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.

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.

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention. Though large efforts have been made, they may fail to describe the multiple aspects of similarity between social users, as only a single vector for one unique aspect has been represented for each node. To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. Specifically, we first design a binary mask layer to divide the single vector as conditional embeddings for multiple behaviors. Then, we introduce the attention network to model interaction relationship among multiple preferences, and further utilize the adapted message sending and receiving operation of graph neural network, so that multi-aspect preference information from high-order neighbors will be captured. Finally, we utilize Bayesian Personalized Ranking loss function to learn the preference similarity on each behavior, and jointly learn multiple conditional node embeddings via multi-task learning framework. Extensive experiments on public datasets validate that our MCNE framework could significantly outperform several state-of-the-art baselines, and further support the visualization and transfer learning tasks with excellent interpretability and robustness.

Understanding learning materials (e.g. test questions) is a crucial issue in online learning systems, which can promote many applications in education domain. Unfortunately, many supervised approaches suffer from the problem of scarce human labeled data, whereas abundant unlabeled resources are highly underutilized. To alleviate this problem, an effective solution is to use pre-trained representations for question understanding. However, existing pre-training methods in NLP area are infeasible to learn test question representations due to several domain-specific characteristics in education. First, questions usually comprise of heterogeneous data including content text, images and side information. Second, there exists both basic linguistic information as well as domain logic and knowledge. To this end, in this paper, we propose a novel pre-training method, namely QuesNet, for comprehensively learning question representations. Specifically, we first design a unified framework to aggregate question information with its heterogeneous inputs into a comprehensive vector. Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way. Here, a novel holed language model objective is developed to extract low-level linguistic features, and a domain-oriented objective is proposed to learn high-level logic and knowledge. Moreover, we show that QuesNet has good capability of being fine-tuned in many question-based tasks. We conduct extensive experiments on large-scale real-world question data, where the experimental results clearly demonstrate the effectiveness of QuesNet for question understanding as well as its superior applicability.

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.

Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities. Here, a theme community is a cohesive subgraph such that a common pattern is frequent in all transaction databases associated with the vertices in the subgraph. Finding all theme communities from a database network enjoys many novel applications. However, it is challenging since even counting the number of all theme communities in a database network is #P-hard. Inspired by the observation that a theme community shrinks when the length of the pattern increases, we investigate several properties of theme communities and develop TCFI, a scalable algorithm that uses these properties to effectively prune the patterns that cannot form any theme community. We also design TC-Tree, a scalable algorithm that decomposes and indexes theme communities efficiently. Retrieving a ranked list of theme communities from a TC-Tree of hundreds of millions of theme communities takes less than 1 second. Extensive experiments and a case study demonstrate the effectiveness and scalability of TCFI and TC-Tree in discovering and querying meaningful theme communities from large database networks.

Clustering is a powerful approach to analyze gene expression data which is crucial to the investigation of effective treatment of cancer. Many graph regularize-based clustering methods have been proposed and shown to be superior to the traditional clustering methods. However, they only focus on the inner structure in samples and fail to take the feature manifold into account. In gene expression data, it's practical to hypothesize that both the samples and the genes lie on nonlinear low dimensional manifolds, namely sample manifold and gene manifold, respectively. Therefore in this paper, incorporating the geometric structures in both samples and features, we propose a Dual Hypergraph Regularized PCA (DHPCA) method for biclustering of tumor data. First, for gene expression data, we construct two hypergraphs, i.e., sample hypergraph and gene hypergraph, to estimate the intrinsic geometric structures of samples and genes. Then, we introduce the hypergraph regularization on both gene side and sample side. Finally, our biclustering method is formulated as two hypergraph regularized PCA with closed-form solution. We experimentally validate our proposed DHPCA algorithm on real applications and the promising results indicate its potential in high dimension data analysis.

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 users implicitly express their preferences to items on many real-world applications, the implicit feedback based collaborative filtering has attracted much attention in recent years. Pairwise methods have shown state-of-the-art solutions for dealing with the implicit feedback, with the assumption that users prefer the observed items to the unobserved items. However, for each user, the huge unobserved items are not equal to represent her preference. In this paper, we propose a Multiple Pairwise Ranking (MPR) approach, which relaxes the simple pairwise preference assumption in previous works by further tapping the connections among items with multiple pairwise ranking criteria. Specifically, we exploit the preference difference among multiple pairs of items by dividing the unobserved items into different parts. Empirical studies show that our algorithms outperform the state-of-the-art methods on real-world datasets.

Dense subgraph discovery is a key primitive in many graph mining applications, such as detecting communities in social networks and mining gene correlation from biological data. Most studies on dense subgraph mining only deal with one graph. However, in many applications, we have more than one graph describing relations among a same group of entities. In this paper, given two graphs sharing the same set of vertices, we investigate the problem of detecting subgraphs that contrast the most with respect to density. We call such subgraphs Density Contrast Subgraphs, or DCS in short. Two widely used graph density measures, average degree and graph affinity, are considered. For both density measures, mining DCS is equivalent to mining the densest subgraph from a "difference" graph, which may have both positive and negative edge weights. Due to the existence of negative edge weights, existing dense subgraph detection algorithms cannot identify the subgraph we need. We prove the computational hardness of mining DCS under the two graph density measures and develop efficient algorithms to find DCS. We also conduct extensive experiments on several real-world datasets to evaluate our algorithms. The experimental results show that our algorithms are both effective and efficient.

Crowdfunding is an emerging online fundraising mechanism for creators to launch campaigns (projects) to solicit funds or expand their influence. Tracking the dynamics, i.e., daily funding amounts can be of great help to campaign creators as well as contributors. Previous works on this subject either fit the fluctuations of time-series with predefined stochastic process or apply a regularization term to constrain learned tendencies, resulting in limited generalization abilities. Patterns of funding-amount sequences in crowdfunding are often exclusive and non-linear, making previous predictors suboptimal. To tackle this problem, we propose a novel method based on synthesized bases which can be composed into arbitrary patterns. Concretely, we build a large set of candidate basis from which we select based on reliability, diversity and latent structures. We use representations of sequences in this basis space as a predictor, and adopt a dual-graph to exploit neighbouring information to enhance its prediction quality. Experimental results demonstrate the effectiveness of our method.

Recently, the Network Representation Learning (NRL) techniques, which target at learning the low-dimension vector representation of graph structures, have attracted wide attention due to the effectiveness on various social-oriented application. Though large efforts have been made on the joint analysis combining node attributes with the network structure, they may usually fail to summarize the weighted correlations within nodes and attributes, especially when the nodes suffer extremely sparse attributes. To that end, in this paper, we propose a novel Sparse Attributed Network Embedding (SANE) framework to learn the network structure and sparse attribute information simultaneously in a united approach. Specifically, we first embed the nodes and attributes into a low-dimensional vector space. Then we introduce the pairwise method to capture the interaction between nodes and sparse attributes, and aggregate the attribute information of neighbors to alleviate sparsity for obtaining a better vector representation of node embeddings, which will be used in following network representation learning task. Along this line, we maintain the network structure by maximizing the probability of predicting the center node according to surrounding context nodes. Different from previous work, we introduce an attention mechanism to adaptively weigh the strength of interactions between each context node and the center node, according to the node attribute similarity. Furthermore, we combine the attention network with CBOW model to learn the similarity of the network structure and node attributes simultaneously. Extensive experiments on public datasets have validated the effectiveness of our SANE model with significant margin compared with the state-of-the-art baselines, which demonstrates the potential of adaptively attribute analysis in network embedding.

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.

Natural Language Inference (NLI) task requires an agent to determine the semantic relation between a premise sentence (p) and a hypothesis sentence (h), which demands sufficient understanding about sentences from lexical knowledge to global semantic. Due to the issues such as polysemy, ambiguity, as well as fuzziness of sentences, fully understanding sentences is still challenging. To this end, we propose an Image-Enhanced Multi-Level Sentence Representation Net (IEMLRN), a novel architecture that is able to utilize the image to enhance the sentence semantic understanding at different scales. To be specific, we introduce the corresponding image of sentences as reference information, which can be helpful for sentence semantic understanding and inference relation evaluation. Since image information might be related to the sentence semantics at different scales, we design a multi-level architecture to understand sentences from different granularity and generate the sentence representation more precisely. Experimental results on the large-scale NLI corpus and real-world NLI alike corpus demonstrate that IEMLRN can simultaneously improve the performance. It is noteworthy that IEMLRN significantly outperforms the state-of-the-art sentence-encoding based models on the challenging hard subset and challenging lexical subset of SNLI corpus.

Zero-shot learning deals with the problem when the training domain and the test domain have different class sets of image instances. To tackle the problem of some classes in the test data never appeared in the training set, a most popular approach is to map both images and classes in a common space under the embedding based framework. Nevertheless, most embedding based models suffered from the semantic loss problem. Furthermore, the expressive power is limited by representing classes and images as mere points. To tackle these problems, in this paper, we propose an Energy-Based Zero-shot Learning model (EBZL) to encode the association between class attributes and input images for zero-shot learning. EBZL is composed of two parts. The first part is a variational autoencoder that reduces the input dimension of images with representative hidden representations. By feeding the hidden representations as the input of the second part, the second part works as the energy function part based on the deep Boltzmann machine. Specifically, we adapt tradition deep Boltzmann machine to a supervised setting without changing its property as an undirected probabilistic graphic model, which helps to preserve semantic integrity and circumvents semantic loss problem. We further utilize variational inference techniques and mean-field approximation to reduce time complexity in model training process. Finally, extensive experimental results on several real-world datasets clearly show the effectiveness of our proposed method.

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendationsare becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers' long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.

In online education systems, finding similar exercises is a fundamental task of many applications, such as exercise retrieval and student modeling. Several approaches have been proposed for this task by simply using the specific textual content (e.g. the same knowledge concepts or the similar words) in exercises. However, the problem of how to systematically exploit the rich semantic information embedded in multiple heterogenous data (e.g. texts and images) to precisely retrieve similar exercises remains pretty much open. To this end, in this paper, we develop a novel Multimodal Attention-based Neural Network (MANN) framework for finding similar exercises in large-scale online education systems by learning a unified semantic representation from the heterogenous data. In MANN, given exercises with texts, images and knowledge concepts, we first apply a convolutional neural network to extract image representations and use an embedding layer for representing concepts. Then, we design an attention-based long short-term memory network to learn a unified semantic representation of each exercise in a multimodal way. Here, two attention strategies are proposed to capture the associations of texts and images, texts and knowledge concepts, respectively. Moreover, with a Similarity Attention, the similar parts in each exercise pair are also measured. Finally, we develop a pairwise training strategy for returning similar exercises. Extensive experimental results on real-world data clearly validate the effectiveness and the interpretation power of MANN.

Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured code), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what'' solution. Specifically, we first decide "where-to-look'' through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write'' by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine our STN framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.

With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band.

With the development of social networking services, social influence analyses, as well as the influence maximization tasks, have attracted wide attention in both academia and industry. Traditional studies mainly focus on simulating process of influence spread. However, two basic functions of social spread, i.e., information propagation and information adoption have not been clearly distinguished. Usually, as information adoption could be even more significant for information publishers in application scenarios, more comprehensive analysis for effect of adoption is urgently required. To that end, in this paper, we propose a novel framework to generally describe social spread, in which information adoption process is separately formulated as random events. Along this line, when we apply this framework to the information adoption maximization task, with proving that the adoption maximization problem is NP-hard and submodular, we further design a polling-based algorithm to achieve an effective approximation. Extensive experiments on four real-world data sets demonstrate the effectiveness and efficiency of proposed algorithms, which validates that our approach could better summarize the complete social spread process, and further support the necessity of distinguishing information adoption from information propagation.

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.

Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people’s learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling tries to profile examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills). However, to the best of our knowledge, the problem of extracting information from both objective and subjective examination problems to achieve more precise and interpretable cognitive analysis remains underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees’ cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then we combine fuzzy set theory and educational hypotheses to model the examinees’ mastery on the problems based on their skill proficiency. Finally, we simulate the generation of examination score on each problem by considering slip and guess factors. In this way, the whole diagnosis framework is built. For further comprehensive verification, we apply our FuzzyCDF to three classical cognitive assessment tasks, i.e., predicting examinee performance, slip and guess detection, and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for these assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.

As the worlds of commerce and the Internet technology become more inextricably linked, a large number of user consumption series become available for online market intelligence analysis. A critical demand along this line is to predict the future product adoption state of each user, which enables a wide range of applications such as targeted marketing. Nevertheless, previous works only aimed at predicting if a user would adopt a particular product or not with a binary buy-or-not representation. The problem of tracking and predicting users' adoption rates, i.e., the frequency and regularity of using each product over time, is still under-explored. To this end, we present a comprehensive study of product adoption rate prediction in a competitive market. This task is nontrivial as there are three major challenges in modeling users' complex adoption states: the heterogeneous data sources around users, the unique user preference and the competitive product selection. To deal with these challenges, we first introduce a flexible factor-based decision function to capture the change of users' product adoption rate over time, where various factors that may influence users' decisions from heterogeneous data sources can be leveraged. Using this factor-based decision function, we then provide two corresponding models to learn the parameters of the decision function with both generalized and personalized assumptions of users' preferences. We further study how to leverage the competition among different products and simultaneously learn product competition and users' preferences with both generalized and personalized assumptions. Finally, extensive experiments on two real-world datasets show the superiority of our proposed models.

Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage the location’s spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback-based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data; Second, it is strongly connected with widely used neighbor additive models, graph Laplacian regularized models, and collective matrix factorization. Finally, we extensively evaluate GeoMF++ on two large-scale LBSN datasets. The experimental results show that GeoMF++ consistently outperforms the state-of-the-art and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space.

DanMu , an emerging type of user-generated comment, has become increasingly popular in recent years. Many online video platforms such as Tudou.com have provided the DanMu function. Unlike traditional online reviews such as reviews at Youtube.com that are outside the videos, DanMu is a scrolling marquee comment, which is overlaid directly on top of the video and synchronized to a specific playback time. Such comments are displayed as streams of moving subtitles overlaid on the video screen. Viewers could easily write DanMu s while watching videos, and the written DanMu s will be immediately overlaid onto the video and displayed to writers themselves and other viewers as well. Such DanMu systems have greatly enabled users to communicate with each other in a much more direct way, creating a real-time sharing experience. Although there are several unique features of DanMu and has had a great impact on online video systems, to the best of our knowledge, there is no work that has provided a comprehensive study on DanMu . In this article, as a pilot study, we analyze the unique characteristics of DanMu from various perspectives. Specifically, we first illustrate some unique distributions of DanMu s by comparing with traditional reviews (TReviews) that we collected from a real DanMu -enabled online video system. Second, we discover two interesting patterns in DanMu data: a herding effect and multiple-burst phenomena that are significantly different from those in TRviews and reveal important insights about the growth of DanMu s on a video. Towards exploring antecedents of both th herding effect and multiple-burst phenomena, we propose to further detect leading DanMu s within bursts, because those leading DanMu s make the most contribution to both patterns. A framework is proposed to detect leading DanMu s that effectively combines multiple factors contributing to leading DanMu s. Based on the identified characteristics of DanMu , finally we propose to predict the distribution of future DanMu s (i.e., the growth of DanMu s), which is important for many DanMu -enabled online video systems, for example, the predicted DanMu distribution could be an indicator of video popularity. This prediction task includes two aspects: One is to predict which videos future DanMu s will be posted for, and the other one is to predict which segments of a video future DanMu s will be posted on. We develop two sophisticated models to solve both problems. Finally, intensive experiments are conducted with a real-world dataset to validate all methods developed in this article.

Diagnosing students' knowledge proficiency, i.e., the mastery degrees of a particular knowledge point in exercises, is a crucial issue for numerous educational applications, e.g., targeted knowledge training and exercise recommendation. Educational theories have converged that students learn and forget knowledge from time to time. Thus, it is necessary to track their mastery of knowledge over time. However, traditional methods in this area either ignored the explanatory power of the diagnosis results on knowledge points or relied on a static assumption. To this end, in this paper, we devise an explanatory probabilistic approach to track the knowledge proficiency of students over time by leveraging educational priors. Specifically, we first associate each exercise with a knowledge vector in which each element represents an explicit knowledge point by leveraging educational priors (i.e., Q-matrix ). Correspondingly, each student is represented as a knowledge vector at each time in a same knowledge space. Second, given the student knowledge vector over time, we borrow two classical educational theories (i.e., Learning curve and Forgetting curve ) as priors to capture the change of each student's proficiency over time. After that, we design a probabilistic matrix factorization framework by combining student and exercise priors for tracking student knowledge proficiency. Extensive experiments on three real-world datasets demonstrate both the effectiveness and explanatory power of our proposed model.

In online advertising, Click-Through Rate (CTR) prediction is a crucial task, as it may benefit the ranking and pricing of online ads. To the best of our knowledge, most of the existing CTR prediction methods are shallow layer models (e.g., Logistic Regression and Factorization Machines) or deep layer models (e.g., Neural Networks). Unfortunately, the shallow layer models cannot capture or utilize high-order nonlinear features in ad data. On the other side, the deep layer models cannot satisfy the necessity of updating CTR models online efficiently due to their high computational complexity. To address the shortcomings above, in this paper, we propose a novel hybrid method based on feature learning of both Deep and Shallow Layers (DSL). In DSL, we utilize Deep Neural Network as a deep layer model trained offline to learn high-order nonlinear features and use Factorization Machines as a shallow layer model for CTR prediction. Furthermore, we also develop an online learning implementation based on DSL, i.e., onlineDSL. Extensive experiments on large-scale real-world datasets clearly validate the effectiveness of our DSL method and onlineDSL algorithm compared with several state-of-the-art baselines.

It is well recognized that air quality inference is of great importance for environmental protection. However, due to the limited monitoring stations and various impact factors, e.g., meteorology, traffic volume and human mobility, inference of air quality index (AQI) could be a difficult task. Recently, with the development of new ways for collecting and integrating urban, mobile, and public service data, there is a potential to leverage spatial relatedness and temporal dependencies for better AQI estimation. To that end, in this paper, we exploit a novel spatio-temporal multi-task learning strategy and develop an enhanced framework for AQI inference. Specifically, both time dependence within a single monitoring station, and spatial relatedness across all the stations will be captured, and then well trained with effective optimization to support AQI inference tasks. As air-quality related features from cross-domain data have been extracted and quantified, comprehensive experiments based on real-world datasets validate the effectiveness of our proposed framework with significant margin compared with several state-of-the-art baselines, which support the hypothesis that our spatio-temporal multi-task learning framework could better predict and interpret AQI fluctuation.

Crowdfunding is an emerging Internet fundraising mechanism by raising monetary contributions from the crowd for projects or ventures. In these platforms, the dynamics, i.e., daily funding amount on campaigns and perks (backing options with rewards), are the most concerned issue for creators, backers and platforms. However, tracking the dynamics in crowdfunding is very challenging and still under-explored. To that end, in this paper, we present a focused study on this important problem. A special goal is to forecast the funding amount for a given campaign and its perks in the future days. Specifically, we formalize the dynamics in crowdfunding as a hierarchical time series, i.e., campaign level and perk level. Specific to each level, we develop a special regression by modeling the decision making process of the crowd (visitors and backing probability) and exploring various factors that impact the decision; on this basis, an enhanced switching regression is proposed at each level to address the heterogeneity of funding sequences. Further, we employ a revision matrix to combine the two-level base forecasts for the final forecasting. We conduct extensive experiments on a real-world crowdfunding data collected from Indiegogo.com. The experimental results clearly demonstrate the effectiveness of our approaches on tracking the dynamics in crowdfunding.

RDF is one of the most commonly used knowledge representation forms. Many highly influential knowledge bases, such as Freebase and PubChemRDF, are in RDF format. An RDF data set is usually represented as a collection of subject-predicate-object triples. Despite the flexibility of RDF triples, it is challenging to serve SPARQL queries on RDF data efficiently by directly managing triples due to the following two reasons. First, heavy joins on a large number of triples are needed for query processing, resulting in a large number of data scans and large redundant intermediate results; Second, weakly-typed triple representation provides suboptimal random access - typically with logarithmic complexity. This data access challenge, unfortunately, cannot be easily met by a better query optimizer as large graph processing is extremely I/O-intensive. In this paper, we argue that strongly-typed graph representation is the key to high-performance RDF query processing. We propose Stylus - a strongly-typed store for serving massive RDF data. Stylus exploits a strongly-typed storage scheme to boost the performance of RDF query processing. The storage scheme is essentially a materialized join view on entities, it thus can eliminate a large number of unnecessary joins on triples. Moreover, it is equipped with a compact representation for intermediate results and an efficient graph-decomposition based query planner. Experimental results on both synthetic and real-life RDF data sets confirm that the proposed approach can dramatically boost the performance of SPARQL query processing.

Microblogging Web sites, such as Twitter and Sina Weibo, have become popular platforms for socializing and sharing information in recent years. Spammers have also discovered this new opportunity to unfairly overpower normal users with unsolicited content, namely social spams. Although it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users seeking spammers on purpose is also observed. We regard this behavior as useful information for spammer detection. In this article, we approach the problem of spammer detection by leveraging the “carefulness” of users, which indicates how careful a user is when she is about to follow a potential spammer. We propose a framework to measure the carefulness and develop a supervised learning algorithm to estimate it based on known spammers and legitimate users. We illustrate how the robustness of the detection algorithms can be improved with aid of the proposed measure. Evaluation on two real datasets from Sina Weibo and Twitter with millions of users are performed, as well as an online test on Sina Weibo. The results show that our approach indeed captures the carefulness, and it is effective for detecting spammers. In addition, we find that our measure is also beneficial for other applications, such as link prediction.

P2P lending is an emerging Internet-based application where individuals can directly borrow money from each other. The past decade has witnessed the rapid development and prevalence of online P2P lending platforms, examples of which include Prosper, LendingClub, and Kiva. Meanwhile, extensive research has been done that mainly focuses on the studies of platform mechanisms and transaction data. In this article, we provide a comprehensive survey on the research about P2P lending, which, to the best of our knowledge, is the first focused effort in this field. Specifically, we first provide a systematic taxonomy for P2P lending by summarizing different types of mainstream platforms and comparing their working mechanisms in detail. Then, we review and organize the recent advances on P2P lending from various perspectives (e.g., economics and sociology perspective, and data-driven perspective). Finally, we propose our opinions on the prospects of P2P lending and suggest some future research directions in this field. Meanwhile, throughout this paper, some analysis on real-world data collected from Prosper and Kiva are also conducted.

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.

In a social network, even about the same information the excitement between different users are different. If we want to spread a piece of new information and maximize the expected total amount of excitement, which seed users should we choose? This problem indeed is substantially different from the renowned influence maximization problem and cannot be tackled using the existing approaches. In this paper, motivated by the demand in a few interesting applications, we model the novel problem of activity maximization, and tackle the problem systematically. We first analyze the complexity and the approximability of the problem. We develop an upper bound and a lower bound that are submodular so that the Sandwich framework can be applied. We then devise a polling-based randomized algorithm that guarantees a data dependent approximation factor. Our experiments on four real data sets clearly verify the effectiveness and scalability of our method, as well as the advantage of our method against the other heuristic methods.

Sociologists have long converged that the evolution of a Social Networking Service(SNS) is driven by the interplay between users' preferences (reflected in user-item interaction behavior) and the social network structure (reflected in user-user interaction behavior). Nevertheless, traditional approaches either modeled these two kinds of behaviors in isolation or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of the dynamic social network structure and users' historical preferences affect the evolution of SNSs. Furthermore, can transforming the underlying social theories in the platform evolution modeling process benefit both behavior prediction tasks? In this paper, we incorporate the underlying social theories to explain and model the evolution of users' two kinds of behaviors in SNSs. Specifically, we present two kinds of representations for users' behaviors: a direct (latent) representation that presumes users' behaviors are represented directly (latently) by their historical behaviors. Under each representation, we associate each user's two kinds of behaviors with two vectors at each time. Then, for each representation, we propose the corresponding learning model to fuse the interplay between users' two kinds of behaviors. Finally, extensive experimental results demonstrate the effectiveness of our proposed models for both user preference prediction and social link suggestion.

In this paper, we tackle a challenging problem inherent in a series of applications: tracking the influential nodes in dynamic networks. Specifically, we model a dynamic network as a stream of edge weight updates. This general model embraces many practical scenarios as special cases, such as edge and node insertions, deletions as well as evolving weighted graphs. Under the popularly adopted linear threshold model and independent cascade model, we consider two essential versions of the problem: finding the nodes whose influences passing a user specified threshold and finding the top-k most influential nodes. Our key idea is to use the polling-based methods and maintain a sample of random RR sets so that we can approximate the influence of nodes with provable quality guarantees. We develop an efficient algorithm that incrementally updates the sample random RR sets against network changes. We also design methods to determine the proper sample sizes for the two versions of the problem so that we can provide strong quality guarantees and, at the same time, be efficient in both space and time. In addition to the thorough theoretical results, our experimental results on five real network data sets clearly demonstrate the effectiveness and efficiency of our algorithms.

Extracting dense subgraphs is an important step in many graph related applications. There is a challenging struggle in exploring the tradeoffs between density and size in subgraphs extracted. More often than not, different methods aim at different specific tradeoffs between the two factors. To the best of our knowledge, no existing method can allow a user to explore the full spectrum of the tradeoffs using a single parameter. In this paper, we investigate this problem systematically. First, since the existing studies cannot find highly compact dense subgraphs, we formulate the problem of finding very dense but relatively small subgraphs. Second, we connect our problem with the existing methods and propose a unified framework that can explore the tradeoffs between density and size of dense subgraphs extracted using a hyper-parameter. We give theoretical upper and lower bounds on the hyper-parameter so that the range where the unified framework can produce non-trivial subgraphs is determined. Third, we develop an efficient quadratic programming method for the unified framework, which is a generalization and extension to the existing methods. We show that optimizing the unified framework is essentially a relaxation of the maximization of a family of density functions. Last, we report a systematic empirical study to verify our findings.

With the rapid development of E-commerce, recent years have witnessed the booming of online advertising industry, which raises extensive concerns of both academic and business circles. Among all the issues, the task of Click-through rates (CTR) prediction plays a central role, as it may influence the ranking and pricing of online ads. To deal with this task, the Factorization Machines (FM) model is designed for better revealing proper combinations of basic features. However, the sparsity of ads transaction data, i.e., a large proportion of zero elements, may severely disturb the performance of FM models. To address this problem, in this paper, we propose a novel Sparse Factorization Machines (SFM) model, in which the Laplace distribution is introduced instead of traditional Gaussian distribution to model the parameters, as Laplace distribution could better fit the sparse data with higher ratio of zero elements. Along this line, it will be beneficial to select the most important features or conjunctions with the proposed SFM model. Furthermore, we develop a distributed implementation of our SFM model on Spark platform to support the prediction task on mass dataset in practice. Comprehensive experiments on two large-scale real-world datasets clearly validate both the effectiveness and efficiency of our SFM model compared with several state-of-the-art baselines, which also proves our assumption that Laplace distribution could be more suitable to describe the online ads transaction data.

An e-commerce website provides a platform for merchants to sell products to customers. While most existing research focuses on providing customers with personalized product suggestions by recommender systems, in this paper, we consider the role of merchants and introduce a parallel problem, i.e., how to select the most valuable customers for a merchant? Accurately answering this question can not only help merchants to gain more profits, but also benefit the ecosystem of e-commence platforms. To deal with this problem, we propose a general approach by taking into consideration the interest and profit of each customer to the merchant, i.e., select the customers who are not only interested in the merchant to ensure the visit of the merchant, but also capable of making good profits. Specifically, we first generate candidate customers for a given merchant by using traditional recommendation techniques. Then we select a set of the valuable customers from candidate customers, which has the balanced maximization between the interest and the profit metrics. Given the NP-hardness of the balanced maximization formulation, we further introduce efficient techniques to solve this maximization problem by exploiting the inherent submodularity property. Finally, extensive experimental results on a real-world dataset demonstrate the effectiveness of our proposed approach.

Matrix completion is the task of recovering a data matrix from a sample of entries, and has received significant attention in theory and practice. Normally, matrix completion considers a single matrix, which can be a noisy image or a rating matrix in recommendation. In practice however, data is often obtained from multiple domains rather than a single domain. For example, in recommendation, multiple matrices may exist as user x movie and user x book, while correlations among the multiple domains can be reasonably exploited to improve the quality of matrix completion. In this paper, we consider the problem of aligned matrix completion, where multiple matrices are recovered that correspond to different representations of the same group of objects. In the proposed model, we maintain consistency of multiple domains with a shared latent structure, while allowing independent patterns for each separate domain. In addition, we impose the low-rank structure of a matrix with a novel regularizer which provides better approximation than the standard nuclear norm relaxation.

Group-oriented services such as group recommendations aim to provide services for a group of users. For these applications, how to aggregate the preferences of different group members is the toughest yet most important problem. Inspired by game theory, in this paper, we propose to explore the idea of Nash equilibrium to simulate the selections of members in a group by a game process. Along this line, we first compute the preferences (group-dependent optimal selections) of each individual member in a given group scene, i.e., an equilibrium solution of this group, with the help of two pruning approaches. Then, to get the aggregated unitary preference of each group from all group members, we design a matrix factorization-based method which aggregates the preferences in latent space and estimates the final group preference in rating space. After obtaining the group preference, group-oriented services (e.g., group recommendation) can be directly provided. Finally, we construct extensive experiments on two real-world data sets from multiple aspects. The results clearly demonstrate the effectiveness of our method.

Given a signed network where edges are weighted in real number, and positive weights indicate cohesion between vertices and negative weights indicate opposition, we are interested in finding k-Oppositive Cohesive Groups (k-OCG). Each k-OCG is a group of k subgraphs such that (1) the edges within each subgraph are dense and cohesive; and (2) the edges crossing different subgraphs are dense and oppositive. Finding k-OCGs is challenging since the subgraphs are often small, there are multiple k-OCGs in a large signed network, and many existing dense subgraph extraction methods cannot handle edges of two signs. We model k-OCG finding task as a quadratic optimization problem. However, the classical Proximal Gradient method is very costly since it has to use the entire adjacency matrix, which is huge on large networks. Thus, we develop FOCG, an algorithm that is two orders of magnitudes faster than the Proximal Gradient method. The main idea is to only search in small subgraphs and thus avoids using a major portion of the adjacency matrix. Our experimental results on synthetic and real data sets as well as a case study clearly demonstrate the effectiveness and efficiency of our method.

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.

P2P lending is an emerging wealth-management service for individuals, which allows lenders to directly bid and invest on the loans created by borrowers. In these platforms, lenders often pursue multiple objectives (e.g., non-default probability, fully-funded probability and winning-bid probability) when they select loans to invest. How to automatically assess loans from these objectives and help lenders select loan portfolios is a very important but challenging problem. To that end, in this paper, we present a holistic study on portfolio selections in P2P lending. Specifically, we first propose to adapt gradient boosting decision tree, which combines both static features and dynamic features, to assess loans from multiple objectives. Then, we propose two strategies, i.e., weighted objective optimization strategy and multi-objective optimization strategy, to select portfolios for lenders. For each lender, the first strategy attempts to provide one optimal portfolio while the second strategy attempts to provide a Pareto-optimal portfolio set. Further, we design two algorithms, namely DPA and EVA, which can efficiently resolve the optimizations in these two strategies, respectively. Finally, extensive experiments on a large-scale real-world data set demonstrate the effectiveness of our solutions.

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.

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.

Collaborative filtering (CF) models offer users personalized recommendations by measuring the relevance between the active user and each individual candidate item. Following this idea, user-based collaborative filtering (UCF) usually selects the local popular items from the like-minded neighbor users. However, these traditional relevance-based models only consider the individuals (i.e., each neighbor user and candidate item) separately during neighbor set selection and recommendation set generation, thus usually incurring highly similar recommendations that lack diversity. While many researchers have recognized the importance of diversified recommendations, the proposed solutions either needed additional semantic information of items or decreased accuracy in this process. In this article, we describe how to generate both accurate and diversified recommendations from a new perspective. Along this line, we first introduce a simple measure of coverage that quantifies the usefulness of the whole set, that is, the neighbor userset and the recommended itemset as a complete entity. Then we propose a recommendation framework named REC that considers both traditional relevance-based scores and the new coverage measure based on UCF. Under REC, we further prove that the goals of maximizing relevance and coverage measures simultaneously in both the neighbor set selection step and the recommendation set generation step are NP-hard. Luckily, we can solve them effectively and efficiently by exploiting the inherent submodular property. Furthermore, we generalize the coverage notion and the REC framework from both a data perspective and an algorithm perspective. Finally, extensive experimental results on three real-world datasets show that the REC-based recommendation models can naturally generate more diversified recommendations without decreasing accuracy compared to some state-of-the-art models.

With the booming popularity of online social networks like Twitter and Weibo, online user footprints are accumulating rapidly on the social web. Simultaneously, the question of how to leverage the large-scale user-generated social media data for personal credit scoring comes into the sight of both researchers and practitioners. It has also become a topic of great importance and growing interest in the P2P lending industry. However, compared with traditional financial data, heterogeneous social data presents both opportunities and challenges for personal credit scoring. In this article, we seek a deep understanding of how to learn users’ credit labels from social data in a comprehensive and efficient way. Particularly, we explore the social-data-based credit scoring problem under the micro-blogging setting for its open, simple, and real-time nature. To identify credit-related evidence hidden in social data, we choose to conduct an analytical and empirical study on a large-scale dataset from Weibo, the largest and most popular tweet-style website in China. Summarizing results from existing credit scoring literature, we first propose three social-data-based credit scoring principles as guidelines for in-depth exploration. In addition, we glean six credit-related insights arising from empirical observations of the testbed dataset. Based on the proposed principles and insights, we extract prediction features mainly from three categories of users’ social data, including demographics, tweets, and networks. To harness this broad range of features, we put forward a two-tier stacking and boosting enhanced ensemble learning framework. Quantitative investigation of the extracted features shows that online social media data does have good potential in discriminating good credit users from bad. Furthermore, we perform experiments on the real-world Weibo dataset consisting of more than 7.3 million tweets and 200,000 users whose credit labels are known through our third-party partner. Experimental results show that (i) our approach achieves a roughly 0.625 AUC value with all the proposed social features as input, and (ii) our learning algorithm can outperform traditional credit scoring methods by as much as 17% for social-data-based personal credit scoring.

With the pervasive use of GPS-enabled smart phones, location-based services, e.g., Location Based Social Networking (LBSN) have emerged . Point-of-Interests (POIs) Recommendation, as a typical component in LBSN, provides additional values to both customers and merchants in terms of user experience and business turnover. Existing POI recommendation systems mainly adopt Collaborative Filtering (CF), which only exploits user given ratings (i.e., user overall evaluation) about a merchant while regardless of the user preference difference across multiple aspects, which exists commonly in real scenarios. Meanwhile, besides ratings, most LBSNs also provide the review function to allow customers to give their opinions when dealing with merchants, which is often overlooked in these recommender systems. In this demo, we present MARS, a novel POI recommender system based on multi-aspect user preference learning from reviews by using utility theory. We first introduce the organization of our system, and then show how the user preferences across multiple aspects are integrated into our system alongside several case studies of mining user preference and POI recommendations.

The problem of information source detection, whose goal is to identify the source of a piece of information from a diffusion process (e.g., computer virus, rumor, epidemic, and so on), has attracted ever-increasing attention from research community in recent years. Although various methods have been proposed, such as those based on centrality, spectral and belief propagation, the existing solutions still suffer from high time complexity and inadequate effectiveness. To this end, we revisit this problem in the paper and present a comprehensive study from the perspective of likelihood approximation. Different from many previous works, we consider both infected and uninfected nodes to estimate the likelihood for the detection. Specifically, we propose a Maximum A Posteriori (MAP) estimator to detect the information source for general graphs with rumor centrality as the prior. To further improve the efficiency, we design two approximate estimators, namely Brute Force Search Approximation (BFSA) and Greedy Search Bound Approximation (GSBA). BFSA tries to traverse the permitted permutations and directly computes the likelihood, while GSBA exploits a strategy of greedy search to find a surrogate upper bound of the probabilities of permitted permutations for a given node, and derives an approximate MAP estimator. Extensive experiments on several network data sets clearly demonstrate the effectiveness of our methods in detecting the single information source.

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.

With the pervasive use of mobile devices, Location Based Social Networks(LBSNs) have emerged in past years. These LBSNs, allowing their users to share personal experiences and opinions on visited merchants, have very rich and useful information which enables a new breed of location-based services, namely, Merchant Recommendation. Existing techniques for merchant recommendation simply treat each merchant as an item and apply conventional recommendation algorithms, e.g., Collaborative Filtering, to recommend merchants to a target user. However, they do not differentiate the user's real preferences on various aspects, and thus can only achieve limited success. In this paper, we aim to address this problem by utilizing and analyzing user reviews to discover user preferences in different aspects. Following the intuition that a user rating represents a personalized rational choice, we propose a novel utility-based approach by combining collaborative and individual views to estimate user preference (i.e., rating). An optimization algorithm based on a Gaussian model is developed to train our merchant recommendation approach. Lastly we evaluate the proposed approach in terms of effectiveness, efficiency and cold-start using two real-world datasets. The experimental results show that our approach outperforms the state-of-the-art methods. Meanwhile, a real mobile application is implemented to demonstrate the practicability of our method.

With the recent advances in information networks, the problem of identifying group structure or communities has received a significant amount of attention. Most of the existing principles of community detection or clustering mainly focus on either the topological structure of a network or the node attributes separately, while both of the two aspects provide valuable information to characterize the nature of communities. In this paper we combine the topological structure of a network as well as the content information of nodes in the task of detecting communities in information networks. Specifically, we treat a network as a dynamic system and consider its community structure as a consequence of interactions among nodes. To model the interactions we introduce the principle of content propagation and integrate the aspects of structure and content in a network naturally. We further describe the interactions among nodes in two different ways, including a linear model to approximate influence propagation, and modeling the interactions directly with random walk. Based on interaction modeling, the nature of communities is described by analyzing the stable status of the dynamic system. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed framework over the state of the art.

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.

The task of feature selection is to select a subset of the original features according to certain predefined criterion with the goal to remove irrelevant and redundant features, improve the prediction performance and reduce the computational costs of data mining algorithms. In this paper, we integrate feature relevance and redundancy explicitly in the feature selection criterion. Spectral feature analysis is applied here which can fit into both supervised and unsupervised learning problems. Specifically, we formulate the problem into a combinatorial problem to maximize the relevance and minimize the redundancy of the selected subset of features at the same time. The problem can be relaxed and solved with an efficient extended power method with global convergence guaranteed. Extensive experiments demonstrate the advantages of the proposed technique in terms of improving the prediction performance and reducing redundancy in data.

Mobility prediction enables appealing proactive experiences for location-aware services and offers essential intelligence to business and governments. Recent studies suggest that human mobility is highly regular and predictable. Additionally, social conformity theory indicates that people's movements are influenced by others. However, existing approaches for location prediction fail to organically combine both the regularity and conformity of human mobility in a unified model, and lack the capacity to incorporate heterogeneous mobility datasets to boost prediction performance. To address these challenges, in this paper we propose a hybrid predictive model integrating both the regularity and conformity of human mobility as well as their mutual reinforcement. In addition, we further elevate the predictive power of our model by learning location profiles from heterogeneous mobility datasets based on a gravity model. We evaluate the proposed model using several city-scale mobility datasets including location check-ins, GPS trajectories of taxis, and public transit data. The experimental results validate that our model significantly outperforms state-of-the-art approaches for mobility prediction in terms of multiple metrics such as accuracy and percentile rank. The results also suggest that the predictability of human mobility is time-varying, e.g., the overall predictability is higher on workdays than holidays while predicting users' unvisited locations is more challenging for workdays than holidays.

With the explosive growth of social media, it has gained significantly increasing attention from both journalists and their readership in recent years by enhancing the reading experience with its timeliness, high participation, interactivity, etc. On the other hand, the popularity of social media services such as Twitter also leads to the challenge of information overload by generating thousands of responses (tweets) for each article of hot news, which will be overwhelming for readers. In this paper, we address the problem of selecting a representative subset of responses to news in order to deliver the most important information. We consider different criteria regarding the importance of the selected subset, and treat the problem from the data reconstruction perspective with concerns for both quality and generalizability of the selection. The intuition behind our work is that a good selection should be relevant from two levels: i) at the message level, it brings readers new information as much as possible or generalizes other people's opinions comprehensively; ii) at the text level, it is able to reconstruct the corpus. Specifically, the task of selecting responses to news can be formulated as a convex optimization problem where sparse non-negative weights are introduced for all the responses indicating whether they are selected or not. Several gradient based optimization and step size selection methods are also investigated in this paper to achieve a faster rate of convergence. More importantly, the proposed framework evaluates the utility of a set of responses jointly and therefore is able to reduce redundancy of the selected responses. We evaluate our approach on real-world data obtained from Twitter, and the results demonstrate superior performance over the state of the art in both accuracy and generalizability.

As the worlds of commerce and Internet technology become more inextricably linked, a large number of user consumption series become available for creative use. A critical demand along this line is to predict the future product adoption for the merchants, which enables a wide range of applications such as targeted marketing. However, previous works only aimed at predicting if one user will adopt this product or not; the problem of adoption rate (or percentage of use) prediction for each user is still underexplored due to the complexity of user decision-making process. To that end, in this paper we present a comprehensive study for this product adoption rate prediction problem. Specifically, we first introduce a decision function to capture the change of users' product adoption rate, where various factors that may influence the decision can be generally leveraged. Then, we propose two models to solve this function, the Generalized Adoption Model (GAM) that assumes all users are influenced equally by these factors and the Personalized Adoption Model (PAM) that argues each factor contributes differently among people. Furthermore, we extend the PAM to a totally Bayesian model (BPAM) that can automatically learn all parameters. Finally, extensive experiments on two real-world datasets not only show the improvement of our proposed three models, but also give insights to track the effects of the various factors for product adoption decisions.

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.

The rapid growth of location-based services provide the potential to understand people's mobility pattern at an unprecedented level, which can also enable food-service industry to accurately predict consumer's dining behavior. In this paper, by leveraging users' historical dining pattern, socio-demographic characteristics and restaurants' attributes, we aim at generating the top-K restaurants for a user's next dining. Compared to previous studies in location prediction which mainly focus on regular mobility patterns, we present a novelty-seeking based dining recommender system, termed NDRS, in consideration of both exploration and exploitation. First, we apply a Conditional Random Field (CRF) with additional constraints to infer users' novelty-seeking statuses by considering both spatial-temporal-historical features and users' socio-demographic characteristics. On the one hand, when a user is predicted to be novelty-seeking, by incorporating the influence of restaurants' contextual factors such as price and service quality, we propose a context-aware collaborative filtering method to recommend restaurants she has never visited before. On the other hand, when a user is predicted to be not novelty-seeking, we then present a Hidden Markov Model (HMM) considering the temporal regularity to recommend the previously visited restaurants. To evaluate the performance of each component as well as the whole system, we conduct extensive experiments, with a large dataset we have collected covering the concerned dining related check-ins, users' demographics, and restaurants' attributes. The results reveal that our system is effective for dining recommendation.

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.

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.

For better viral marketing, there has been a lot of research on social influence maximization. However, the problem that who is influenced and how diverse the influenced population is, which is important in real-world marketing, has largely been neglected. To that end, in this paper, we propose to consider the magnitude of influence and the diversity of the influenced crowd simultaneously. Specifically, we formulate it as an optimization problem, i.e., diversified social influence maximization. First, we present a general framework for this problem, under which we construct a class of diversity measures to quantify the diversity of the influenced crowd. Meanwhile, we prove that a simple greedy algorithm guarantees to provide a near-optimal solution to the optimization problem. Furthermore, we relax the problem by focusing on the diversity of the nodes targeted for initial activation, and show how this relaxed form could be used to diversify the results of many heuristics, e.g., PageRank. Finally, we run extensive experiments on two real-world datasets, showing that our formulation is effective in generating diverse results.

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.

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.

Trajectory regression, which aims to predict the travel time of arbitrary trajectories on road networks, attracts significant attention in various applications of traffic systems these years. In this paper, we tackle this problem with a multitask learning (MTL) framework. To take the temporal nature of the problem into consideration, we divide the regression problem into a set of sub-tasks of distinct time periods, then the problem can be treated in a multi-task learning framework. Further, we propose a novel regularization term in which we exploit the block sparse structure to augment the robustness of the model. In addition, we incorporate the spatial smoothness over road links and thus achieve a spatial-temporal framework. An accelerated proximal algorithm is adopted to solve the convex but non-smooth problem, which will converge to the global optimum. Experiments on both synthetic and real data sets demonstrate the effectiveness of the proposed method.

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.

Multi-label learning deals with the problem where each training example is associated with a set of labels simultaneously, with the set of labels corresponding to multiple concepts or semantic meanings. Intuitively, the multiple labels are usually correlated in some semantic space while sharing the same input space. As a consequence, the multi-label learning process can be augmented significantly by exploiting the label correlations effectively. Most of the existing approaches share the limitations in that the label correlations are typically taken as prior knowledge, which may not depict the true dependencies among labels correctly, or they do not adequately address the issue of missing labels. In this paper, we propose an integrated framework that learns the correlations among labels while training the multi-label model simultaneously. Specifically, a low rank structure is adopted to capture the complex correlations among labels. In addition, we incorporate a supplementary label matrix which augments the possibly incomplete label matrix by exploiting the label correlations. An alternating algorithm is then developed to solve the optimization problem. Extensive experiments are conducted on a number of image and text data sets to demonstrate the effectiveness of the proposed approach.

P2P lending is an online platform to make borrowing and investment transactions. A central question on these platforms is how to align the right products with the right investors, thus helping investors to make better decisions. Along this line, tremendous efforts have been devoted to modeling the credits of products and borrowers from an economic perspective. However, these global models are only exploratory in nature and are not practical. In this paper, we focus on the personalized investment recommendation by reconstructing the two steps for investment decision making: what to buy and how much money to pay. Specifically, we first generate a candidate investment recommendation list for each investor that tackles "what to buy" problem. In this process, we consider various unique properties of investment recommendation. Furthermore, according to the portfolio theory, we optimize the shares of each recommended candidate by incorporating the investments an investor currently holds, thus solving the "how much money to pay" problem. Finally, extensive experimental results on a large-scale real world dataset show the effectiveness of our model under various evaluation metrics.

Many of today's online news websites have enabled users to specify different types of emotions (e.g., Angry and shocked) they have after reading news. Compared with traditional user feedbacks such as comments and ratings, these specific emotion annotations are more accurate for expressing users' personal emotions. In this paper, we propose to exploit these users' emotion annotations for online news in order to track the evolution of emotions, which plays an important role in various online services. A critical challenge is how to model emotions with respect to time spans. To this end, we propose a time-aware topic modeling perspective for solving this problem. Specifically, we first develop a model named emotion-Topic over Time (eToT), in which we represent the topics of news as a Beta distribution over time and a multinomial distribution over emotions. Whilee ToT can uncover the latent relationship among news, emotion and time directly, it cannot capture the dynamics of topics. Therefore, we further develop another model named emotion based Dynamic Topic Model (eDTM), where we explore the state space model for tracking the dynamics of topics. In addition, we demonstrate that both eToT and eDTM could enable several potential applications, such as emotion prediction, emotion-based news recommendations and emotion anomaly detections. Finally, we validate the proposed models with extensive experiments with a real-world data set.

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive locations. However, extreme sparsity of user-POI matrices creates a severe challenge. To cope with this challenge, viewing mobility records on location-based social networks (LBSNs) as implicit feedback for POI recommendation, we first propose to exploit weighted matrix factorization for this task since it usually serves collaborative filtering with implicit feedback better. Besides, researchers have recently discovered a spatial clustering phenomenon in human mobility behavior on the LBSNs, i.e., individual visiting locations tend to cluster together, and also demonstrated its effectiveness in POI recommendation, thus we incorporate it into the factorization model. Particularly, we augment users' and POIs' latent factors in the factorization model with activity area vectors of users and influence area vectors of POIs, respectively. Based on such an augmented model, we not only capture the spatial clustering phenomenon in terms of two-dimensional kernel density estimation, but we also explain why the introduction of such a phenomenon into matrix factorization helps to deal with the challenge from matrix sparsity. We then evaluate the proposed algorithm on a large-scale LBSN dataset. The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon into matrix factorization improves recommendation performance.

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.

Influence maximization is the problem of finding a set of seed nodes in social network for maximizing the spread of influence. Traditionally, researchers view influence propagation as a stochastic process and formulate the influence maximization problem as a discrete optimization problem. Thus, most previous works focus on finding efficient and effective heuristic algorithms within the greedy framework. In this paper, we view the influence maximization problem from the perspective of data reconstruction and propose a novel framework named \textsl{Data Reconstruction for Influence Maximization}(DRIM). In our framework, we first construct an influence matrix, each row of which is the influence of a node to other nodes. Then, we select $k$ most informative rows to reconstruct the matrix and the corresponding nodes are the seed nodes which could maximize the influence spread. Finally, we evaluate our framework on two real-world data sets, and the results show that DRIM is at least as effective as the traditional greedy algorithm.

Relevant question retrieval and ranking is a typical task in community question answering (CQA). Existing methods mainly focus on long and syntactically structured queries. However, when an input query is short, the task becomes challenging, due to a lack information regarding user intent. In this paper, we mine different types of user intent from various sources for short queries. With these intent signals, we propose a new intent-based language model. The model takes advantage of both state-of-the-art relevance models and the extra intent information mined from multiple sources. We further employ a state-of-the-art learning-to-rank approach to estimate parameters in the model from training data. Experiments show that by leveraging user intent prediction, our model significantly outperforms the state-of-the-art relevance models in question search.

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.

Given fixed budgets, companies attempt to obtain maximum coverage on a social network by targeting at influential individuals. This viral marketing is often modeled by the independent cascade model. However, identifying the most influential people by computing influence spread is NP-hard, and various approximate algorithms are developed. In this paper, we emphasize the probabilistic nature of influence propagation. We propose to use exact probabilistic solutions and prove an inclusion-exclusion principle for computing influence spread. Our probabilistic solutions can significantly speed up the computation of influence spread. We also give a probabilistic-additive incremental search strategy to solve the influence maximization problem, i.e., to find a subset of individuals that has the largest influence spread in the end. Experiments on real data sets demonstrated the effectiveness and efficiency of our methods.

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.

Recent years have witnessed the increased interests in exploiting influence in social networks for many applications. To the best of our knowledge, from the computational aspect of social influence analysis, most of existing work focus on either describing the influence propagation process or identifying the set of most influential seed nodes. However, these work usually do not distinguish the "independent influence" of each single seed node after removing other seeds. Since it is important to quickly figure out the real contribution of each seed, in this paper we propose to measure the seed's independent influence by a linear social influence model. Specifically, we first describe the linear social influence model, and then define the independent influence under this model for eliminating the "mutual enrichment" between seed nodes. Meanwhile, we find that the influence of a set of nodes is actually the sum of their independent influence, and we also give upper bounds for independent influence. Moreover, these findings are evaluated by two applications, i.e., ranking the seeds by their independent influence and identifying the Top-K influential ones. Finally, the experimental results on several real-world datasets validate the effectiveness and efficiency of the proposed independent social influence measures.

This study addresses the problem of inferring users' employment affiliation information from social activities. It is motivated by the applications which need to monitoring and analyzing the social activities of the employees from a given company, especially their social tracks related to the work and business. It definitely helps to better understand their needs and opinions towards certain business area, so that the account sales targeting these customers in the given company can adjust the sales strategies accordingly. Specifically, in this task we are given a snapshot of a social network and some labeled social users who are the employees of a given company. Our goal is to identify more users from the same company. We formulate this problem as a task of classifying nodes over a graph, and develop a Supervised Label Propagation model. It naturally incorporates the rich set of features for social activities, models the networking effect by label propagation, and learns the feature weights so that the labels are propagated to the right users. To validate its effectiveness, we show our case studies on identifying the employees of "China Telecom" and "China Unicom" from Sina Weibo. The experimental results show that our method significantly outperforms the compared baseline ones.

In this paper, we propose Personalized Markov Embedding (PME), a next-song recommendation strategy for online karaoke users. By modeling the sequential singing behavior, we first embed songs and users into a Euclidean space in which distances between songs and users reflect the strength of their relationships. Then, given each user's last song, we can generate personalized recommendations by ranking the candidate songs according to the embedding. Moreover, PME can be trained without any requirement of content information. Finally, we perform an experimental evaluation on a real world data set provided by ihou.com which is an online karaoke website launched by iFLYTEK, and the results clearly demonstrate the effectiveness of PME.

Capturing the context of a user's query from the previous queries and clicks in the same session leads to a better understanding of the user's information need. A context-aware approach to document reranking, URL recommendation, and query suggestion may substantially improve users' search experience. In this article, we propose a general approach to context-aware search by learning avariable length hidden Markov model(vlHMM) from search sessions extracted from log data. While the mathematical model is powerful, the huge amounts of log data present great challenges. We develop several distributed learning techniques to learn a very large vlHMM under themap-reduceframework. Moreover, we construct feature vectors for each state of the vlHMM model to handle users' novel queries not covered by the training data. We test our approach on a raw dataset consisting of 1.9 billion queries, 2.9 billion clicks, and 1.2 billion search sessions before filtering, and evaluate the effectiveness of the vlHMM learned from the real data on three search applications: document reranking, query suggestion, and URL recommendation. The experiment results validate the effectiveness of vlHMM in the applications of document reranking, URL recommendation, and query suggestion.

Collaborative Filtering(CF) is a popular way to build recommender systems and has been successfully employed in many applications. Generally, two kinds of approaches to CF, the local neighborhood methods and the global matrix factorization models, have been widely studied. Though some previous researches target on combining the complementary advantages of both approaches, the performance is still limited due to the extreme sparsity of the rating data. Therefore, it is necessary to consider more information for better reflecting user preference and item content. To that end, in this paper, by leveraging the extra tagging data, we propose a novel unified two-stage recommendation framework, named Neighborhood-aware Probabilistic Matrix Factorization(NHPMF). Specifically, we first use the tagging data to select neighbors of each user and each item, then add unique Gaussian distributions on each user's(item's) latent feature vector in the matrix factorization to ensure similar users(items) will have similar latent features}. Since the proposed method can effectively explores the external data source(i.e., tagging data) in a unified probabilistic model, it leads to more accurate recommendations. Extensive experimental results on two real world datasets demonstrate that our NHPMF model outperforms the state-of-the-art methods.

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.

An ensemble is composed of a set of base learners that make predictions jointly. The generalization performance of an ensemble has been justified both theoretically and in practice. However, existing ensemble learning methods sometimes produce unnecessarily large ensembles, with an expense of extra computational costs and memory consumption. The purpose of ensemble pruning is to select a subset of base learners with comparable or better prediction performance. In this paper, we formulate the ensemble pruning problem into a combinatorial optimization problem with the goal to maximize the accuracy and diversity at the same time. Solving this problem exactly is computationally hard. Fortunately, we can relax and reformulate it as a constrained eigenvector problem, which can be solved with an efficient algorithm that is guaranteed to converge globally. Convincing experimental results demonstrate that this optimization based ensemble pruning algorithm outperforms the state-of-the-art heuristics in the literature.

Recently, the boom of media contents on the Internet raises challenges in managing them effectively and thus requires automatic media annotation techniques. Motivated by the observation that media contents are usually shared frequently in online communities and thus have a lot of social diffusion records, we propose a novel media annotating approach depending on these social diffusion records instead of metadata. The basic assumption is that the social diffusion records reflect the common interests (CI) between users, which can be analyzed for generating annotations. With this assumption, we present a novel CI-based social diffusion model and translate the automatic annotating task into the CI-based diffusion maximization (CIDM) problem. Moreover, we propose to solve the CIDM problem through two optimization tasks, corresponding to the training and test stages in supervised learning. Extensive experiments on real-world data sets show that our approach can effectively generate high quality annotations, and thus demonstrate the capability of social diffusion analysis in annotating media.

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.

Clipping Web pages, namely extracting the informative clips (areas) from Web pages, has many applications, such as Web printing and e-reading on small handheld devices. Although many existing methods attempt to address this task, most of them can either work only on certain types of Web pages (e.g., news- and blog-like web pages), or perform semi-automatically where extra user efforts are required in adjusting the outputs. The problem of clipping any types of Web pages accurately in a totally automatic way remains pretty much open. To this end in this study we harness the wisdom of the crowds to provide accurate recommendation of informative clips on any given Web pages. Specifically, we leverage the knowledge on how previous users clip similar Web pages, and this knowledge repository can be represented as a transaction database where each transaction contains the clips selected by a user on a certain Web page. Then, we formulate a new pattern mining problem, mining top-1 qualified pattern, on transaction database for this recommendation. Here, the recommendation considers not only the pattern support but also the pattern occupancy (proposed in this work). High support requires that patterns appear frequently in the database, while high occupancy requires that patterns occupy a large portion of the transactions they appear in. Thus, it leads to both precise and complete recommendation. Additionally, we explore the properties on occupancy to further prune the search space for high-efficient pattern mining. Finally, we show the effectiveness of the proposed algorithm on a human-labeled ground truth dataset consisting of 2000 web pages from 100 major Web sites, and demonstrate its efficiency on large synthetic datasets.

Mining the frequently visited places of single mobile users, i.e., significant places, is crucial for supporting personalized location-based services. Most of existing works for significance place mining have a need to take advantage the GPS trajectories of users. However, it is difficult to encourage mobile users to contribute GPS trajectories because of the high power consumption of GPS. In this paper, we propose a geo-grid based approach for mining significant places from cell ID trajectories. In our approach, the mined significant places are represented as sets of geo-grids which are much smaller than the coverage areas of cell-sites. To be specific, we firstly extract the stay areas where the mobile user used to stay and map them to many geo-grids. Then we mine significant places from the geo-grids by considering their significance. We evaluate the approach on real word data sets and the experimental results clearly show that the proposed approach outperforms two baselines.

Mining the frequently visited places of single mobile users, i.e., significant places, is crucial for supporting personalized location-based services. Most of existing works for significance place mining have a need to take advantage the GPS trajectories of users. However, it is difficult to encourage mobile users to contribute GPS trajectories because of the high power consumption of GPS. In this demonstration, we propose a geo-grid based approach for mining significant places from cell ID trajectories. In our approach, the mined significant places are represented as sets of geo-grids which are much smaller than the coverage areas of cell-sites. To be specific, we firstly extract the stay areas where the mobile user used to stay and map them to many geogrids. Then we mine significant places from the geo-grids by considering their significance.

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.

Discovering similar users with respect to their habits plays an important role in a wide range of applications, such as collaborative filtering for recommendation, user segmentation for market analysis, etc. Recently, the progressing ability to sense user contexts of smart mobile devices makes it possible to discover mobile users with similar habits by mining their habits from their mobile devices. However, though some researchers have proposed effective methods for mining user habits such as behavior pattern mining, how to leverage the mined results for discovering similar users remains less explored. To this end, we propose a novel approach for conquering the sparseness of behavior pattern space and thus make it possible to discover similar mobile users with respect to their habits by leveraging behavior pattern mining. To be specific, first, we normalize the raw context log of each user by transforming the location-based context data and user interaction records to more general representations. Second, we take advantage of a constraint-based Bayesian Matrix Factorization model for extracting the latent common habits among behavior patterns and then transforming behavior pattern vectors to the vectors of mined common habits which are in a much more dense space. The experiments conducted on real data sets show that our approach outperforms three baselines in terms of the effectiveness of discovering similar mobile users with respect to their habits.

With the prevalence of smart mobile devices with multiple sensors, the commercial application of intelligent context-aware services becomes more and more attractive. However, limited by the battery capacity, the energy efficiency of context-sensing is the bottleneck for the success of context-aware applications. Though several previous studies for energy-efficient context-sensing have been reported, none of them can be applied to multiple types of high-energy-consuming sensors. Moreover, applying machine learning technologies to energy-efficient context-sensing is underexplored too. In this article, we propose to leverage machine learning technologies for improving the energy efficiency of multiple high-energy-consuming context sensors by trading off the sensing accuracy. To be specific, we try to infer the status of high-energy-consuming sensors according to the outputs of software-based sensors and the physical sensors that are necessary to work all the time for supporting the basic functions of mobile devices. If the inference indicates the high-energy-consuming sensor is in a stable status, we avoid the unnecessary invocation and instead use the latest invoked value as the estimation. The experimental results on real datasets show that the energy efficiency of GPS sensing and audio-level sensing are significantly improved by the proposed approach while the sensing accuracy is over 90%.

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.

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.

Query suggestion plays an important role in improving usability of search engines. Although some recently proposed methods provide query suggestions by mining query patterns from search logs, none of them models the immediately preceding queries as context systematically, and uses context information effectively in query suggestions. Context-aware query suggestion is challenging in both modeling context and scaling up query suggestion using context. In this article, we propose a novel context-aware query suggestion approach. To tackle the challenges, our approach consists of two stages. In the first, offline model-learning stage , to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. A concept sequence suffix tree is then constructed from session data as a context-aware query suggestion model. In the second, online query suggestion stage , a user’s search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence suffix tree, we suggest to the user context-aware queries. We test our approach on large-scale search logs of a commercial search engine containing 4.0 billion Web queries, 5.9 billion clicks, and 1.87 billion search sessions. The experimental results clearly show that our approach outperforms three baseline methods in both coverage and quality of suggestions.

The user interaction with the mobile device plays an important role in user habit understanding. In this paper, we propose to mine the associations between user interactions and contexts captured by mobile devices, or behavior patterns for short, from context logs to characterize the habits of mobile users. The extensive experiments on the collected real life data clearly validate the ability of our approach for mining effective behavior patterns.

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.

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.

The context of a search query often provides a search engine meaningful hints for answering the current query better. Previous studies on context-aware search were either focused on the development of context models or limited to a relatively small scale investigation under a controlled laboratory setting. Particularly, about context-aware ranking for Web search, the following two critical problems are largely remained unsolved. First, how can we take advantage of different types of contexts in ranking? Second, how can we integrate context information into a ranking model? In this paper, we tackle the above two essential problems analytically and empirically. We develop different ranking principles for different types of contexts. Moreover, we adopt a learning-to-rank approach and integrate the ranking principles into a state-of-the-art ranking model by encoding the context information as features of the model. We empirically test our approach using a large search log data set obtained from a major commercial search engine. Our evaluation uses both human judgments and implicit user click data. The experimental results clearly show that our context-aware ranking approach improves the ranking of a commercial search engine which ignores context information. Furthermore, our method outperforms a baseline method which considers context information in ranking.

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.

Understanding users'search intent expressed through their search queries is crucial to Web search and online advertisement. Web query classification (QC) has been widely studied for this purpose. Most previous QC algorithms classify individual queries without considering their context information. However, as exemplified by the well-known example on query "jaguar", many Web queries are short and ambiguous, whose real meanings are uncertain without the context information. In this paper, we incorporate context information into the problem of query classification by using conditional random field (CRF) models. In our approach, we use neighboring queries and their corresponding clicked URLs (Web pages) in search sessions as the context information. We perform extensive experiments on real world search logs and validate the effectiveness and effciency of our approach. We show that we can improve the F1 score by 52% as compared to other state-of-the-art baselines.

Capturing the context of a user's query from the previous queries and clicks in the same session may help understand the user's information need. A context-aware approach to document re-ranking, query suggestion, and URL recommendation may improve users' search experience substantially. In this paper, we propose a general approach to context-aware search. To capture contexts of queries, we learn a variable length Hidden Markov Model (vlHMM) from search sessions extracted from log data. Although the mathematical model is intuitive, how to learn a large vlHMM with millions of states from hundreds of millions of search sessions poses a grand challenge. We develop a strategy for parameter initialization in vlHMM learning which can greatly reduce the number of parameters to be estimated in practice. We also devise a method for distributed vlHMM learning under the map-reduce model. We test our approach on a real data set consisting of 1.8 billion queries, 2.6 billion clicks, and 840 million search sessions, and evaluate the effectiveness of the vlHMM learned from the real data on three search applications: document re-ranking, query suggestion, and URL recommendation. The experimental results show that our approach is both effective and efficient.

Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware - they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offine model-learning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence sufix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1:8 billion search queries, 2:6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions.

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.

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.