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.

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

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

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

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.

Urban land use, intrinsically linked to people’s daily activities, undergoes continuous evolution, presenting a complex interplay that remains partially understood. To bridge this gap, our study leverages fine-grained human mobility data to predict these changes, adopting a novel approach that conceptualizes “community-level” land use shifts as a regression problem and represents citywide changes through dynamic graphs. We harness recent advancements in graph neural networks (GNNs), which, despite their success in various applications, face challenges in directly predicting land use changes due to the temporal mismatch between the slow evolution of urban land and the immediacy of human mobility data. Our research stands out by introducing a temporal skeleton for dynamic GNNs to synchronize human activity graphs with urban land use changes, a dynamic heterogeneous GNN approach for integrating diverse human activity data to capture essential temporal dependencies, and a novel algorithm powered by causal inference to elucidate the primary factors influencing land use predictions at the community level, all of which contribute to a training process informed by the generated causal graph. Empirically validated on three real-world datasets, our model demonstrates a performance leap over state-of-the-art baselines, marking a pivotal step toward understanding and predicting the dynamics of urban land use.

Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices. The data distributed among the edge devices are highly heterogeneous. Thus, FL faces the challenge of data distribution and heterogeneity, where non-Independent and Identically Distributed (non-IID) data across edge devices may yield in significant accuracy drop. Furthermore, the limited computation and communication capabilities of edge devices increase the likelihood of stragglers, thus leading to slow model convergence. In this article, we propose the FedDHAD FL framework, which comes with two novel methods: dynamic heterogeneous model aggregation (FedDH) and adaptive dropout (FedAD). FedDH dynamically adjusts the weights of each local model within the model aggregation process based on the non-IID degree of heterogeneous data to deal with the statistical data heterogeneity. FedAD performs neuron-adaptive operations in response to heterogeneous devices to improve accuracy while achieving superb efficiency. The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and computation cost (up to 15.0% smaller).

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

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

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

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

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

In recommendation systems, new items are continuously introduced, initially lacking interaction records but gradually accumulating them over time.Accurately predicting the click-through rate (CTR) for these items is crucial for enhancing both revenue and user experience.While existing methods focus on enhancing item ID embeddings for new items within general CTR models, they tend to adopt a global feature interaction approach, often overshadowing new items with sparse data by those with abundant interactions.Addressing this, our work introduces EmerG, a novel approach that warms up cold-start CTR prediction by learning item-specific feature interaction patterns.EmerG utilizes hypernetworks to generate an item-specific feature graph based on item characteristics, which is then processed by a Graph Neural Network (GNN).This GNN is specially tailored to provably capture feature interactions at any order through a customized message passing mechanism.We further design a meta learning strategy that optimizes parameters of hypernetworks and GNN across various item CTR prediction tasks, while only adjusting a minimal set of item-specific parameters within each task.This strategy effectively reduces the risk of overfitting when dealing with limited data.Extensive experiments on benchmark datasets validate that EmerG consistently performs the best given no, a few and sufficient instances of new items.

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

\beginabstract Segmenting an urban area into regions is fundamentally important for many spatio-temporal applications. The traditional grid-based method offers a simple solution as it divides the city map into equal-sized grids, but it fails to preserve semantic information about the original urban structure. Several studies apply the road network to cut the metropolitan area into meaningful blocks. However, existing works do not achieve good scalability, and there is no public system provided so far. To address those problems, we build\mysystem, the first scalable vector-based system which generates reasonable regions using all levels of the road network. We conduct an evaluation to prove the efficiency and effectiveness of our system. We also publish our system as a Python library through Python Package Index (PyPI), and demonstrate its utility using real public datasets in this paper. The source code and useful instructions can be found on https://github.com/PaddlePaddle/PaddleSpatial/tree/main/paddlespatial/tools/genregion. \endabstract

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

Recommender systems usually face confounding bias and selection bias. The former arises when hidden variables determine user/item features and an outcome variable simultaneously while the latter happens due to some biased selection mechanisms, e.g., choosing users based on a specific time or location. How to alleviate such biases has attracted a lot research attention in recent years, but existing approaches mainly focus on one specific source of bias, rather than handle both confounding and selection biases. To this end, we formulate the causal personalized recommendation problem based on the structural causal model (SCM) and a generalization of the notion of backdoor adjustment to account for both biases. Our approach leverages external data of some variables that are also measured without selection bias and uses an adjustment pair based on the derived graphical conditions for identifying conditional causal effects. We present a statistical estimation procedure based on inverse probability weighting to calculate conditional causal effects when training samples are limited. In the presence of confounding and selection biases, we also show how to derive path-specific effects and counterfactual effects, both of which are important for recommendation analysis. We demonstrate the effectiveness of our approach through empirical evaluations.

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

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.

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.

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

The past few years have witnessed an explosive growth of user-generated POI-centric travel blogs, which can provide a comprehensive understanding of a POI for people. However, evaluating the quality of the POI-centric travel blogs and ranking the blogs is not a simple task without domain knowledge or actual travel experience on the target POI. Nevertheless, our insight is that the user search behavior related to the target POI on the online map service can partly valid the rationality of the POIs appearing in the travel blogs, which helps for travel blogs ranking. To this end, in this paper, we propose a novel end-to-end framework for travel blogs ranking, coined Matching POI and Travel Blogs with Multi-view InFormation (MOTIF). Concretely, we first construct two POI graphs as multi-view information: (1) the search-level POI graph which reflects the user behaviors on the online map service; and (2) the document-level POI graph which shows the POI co-occurrence frequency in travel blogs. Then, to better model the intrinsic correlation of the two graphs, we adopt Mutual Information Maximization to align the search-level and document-level semantic spaces. Moreover, we leverage a pair-wise ranking loss for POI-document relevance scoring. Extensive experiments on two real-world datasets demonstrate the superiority of our method.

Graph Neural Networks (GNNs) have become widely-used models for semi-supervised learning. However, the robustness of GNNs in the presence of label noise remains a largely under-explored problem. In this paper, we consider an important yet challenging scenario where labels on nodes of graphs are not only noisy but also scarce. In this scenario, the performance of GNNs is prone to degrade due to label noise propagation and insufficient learning. To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise. More specifically, we introduce self-reinforcement and consistency regularization as supplemental supervision. The self-reinforcement supervision is inspired by the memorization effects of deep neural networks and aims to correct noisy labels. Further, the consistency regularization prevents GNNs from overfitting to noisy labels via mimicry loss in both the inter-view and intra-view perspectives. To leverage such supervisions, we divide labels into clean and noisy types, rectify inaccurate labels, and further generate pseudo-labels on unlabeled nodes. Supervision for nodes with different types of labels is then chosen adaptively. This enables sufficient learning from clean labels while limiting the impact of noisy ones. We conduct extensive experiments to evaluate the effectiveness of our RTGNN framework, and the results validate its consistent superior performance over state-of-the-art methods with two types of label noises and various noise rates.

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

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

While online advertising is one of the major sources of income for search engines, pumping up the incomes from business advertisements while ensuring the user experience becomes a challenging but emerging area. Designing high-quality advertisements with persuasive content has been proved as a way to increase revenues through improving the Click-Through Rate (CTR). However, it is difficult to scale up the design of high-quality ads, due to the lack of automation in creativity. In this paper, we present Commonsense-Enriched Advertisement on Search Engine (CHASE) --- a system for the automatic generation of persuasive ads. CHASE adopts a specially designed language model that fuses the keywords, commonsense-related texts, and marketing contents to generate persuasive advertisements. Specifically, the language model has been pre-trained using massive contents of explicit knowledge and fine-tuned with well-constructed quasi-parallel corpora with effective control of the proportion of commonsense in the generated ads and fitness to the ads' keywords. The effectiveness of the proposed method CHASE has been verified by real-world web traffics for search and manual evaluation. In A/B tests, the advertisements generated by CHASE would bring 11.13% CTR improvement. The proposed model has been deployed to cover three advertisement domains (which are kid education, psychological counseling, and beauty e-commerce) at Baidu, the world's largest Chinese search engine, with adding revenue of about 1 million RMB (Chinese Yuan) per day.

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

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

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

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

While Water Treatment Networks (WTNs) are critical infrastructures for local communities and public health, WTNs are vulnerable to cyber attacks. Effective detection of attacks can defend WTNs against discharging contaminated water, denying access, destroying equipment, and causing public fear. While there are extensive studies in WTNs attack detection, they only exploit the data characteristics partially to detect cyber attacks. After preliminary exploring the sensing data of WTNs, we find that integrating spatio-temporal knowledge, representation learning, and detection algorithms can improve attack detection accuracy. To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatiotemporal characteristics of cyber attacks in WTNs. In particular, we propose a spatio-temporal representation framework specially tailored to cyber attacks after separating the sensing data of WTNs into a sequence of time segments. This framework has two key components. The first component is a temporal embedding module to preserve temporal patterns within a time segment by projecting the time segment of a sensor into a temporal embedding vector. We then construct Spatio-Temporal Graphs (STGs), where a node is a sensor and an attribute is the temporal embedding vector of the sensor, to describe the state of the WTNs. The second component is a spatial embedding module, which learns the final fused embedding of the WTNs from STGs. In addition, we devise an improved one class-SVM model that utilizes a new designed pairwise kernel to detect cyber attacks. The devised pairwise kernel augments the distance between normal and attack patterns in the fused embedding space. Finally, we conducted extensive experimental evaluations with real-world data to demonstrate the effectiveness of our framework: it achieves an accuracy of 91.65%, with average improvement ratios of 82.78% and 22.96% with respect to F1 and AUC, compared with baseline methods.

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

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

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

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

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

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

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

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

Choosing a good location when opening a new store is crucial for the future success of a business. Traditional methods include offline manual survey, analytic models based on census data, which are either unable to adapt to the dynamic market or very time consuming. The rapid increase of the availability of big data from various types of mobile devices, such as online query data and offline positioning data, provides us with the possibility to develop automatic and accurate data- driven prediction models for business store site selection. In this paper, we propose a Demand Driven Store Site Selection (DD3S) framework for business store site selection by mining search query data from Baidu Maps. DD3S first detects the spatial-temporal distributions of customer demands on different business services via query data from Baidu Maps, the largest online map search engine in China, and detects the gaps between demand and supply. Then we determine candidate locations via clustering such gaps. In the final stage, we solve the location optimization problem by predicting and ranking the number of customers. We not only deploy supervised regression models to predict the number of customers, but also use learning-to-rank model to directly rank the locations. We evaluate our framework on various types of businesses in real-world cases, and the experiment results demonstrate the effectiveness of our methods. DD3S as the core function for store site selection has already been implemented as a core component of our business analytics platform and could be potentially used by chain store merchants on Baidu Nuomi.