Next point-of-interest (POI) recommendation improves personalized location-based services by predicting users' next destinations based on their historical check-ins. However, most existing methods rely on static datasets and fixed models, limiting their ability to adapt to changes in user behavior over time. To address this limitation, we explore a novel task termed continual next POI recommendation, where models dynamically adapt to evolving user interests through continual updates. This task is particularly challenging, as it requires capturing shifting user behaviors while retaining previously learned knowledge. Moreover, it is essential to ensure efficiency in update time and memory usage for real-world deployment. To this end, we propose GIRAM (Generative Key-based Interest Retrieval and Adaptive Modeling), an efficient, model-agnostic framework that integrates context-aware sustained interests with recent interests. GIRAM comprises four components: (1) an interest memory to preserve historical preferences; (2) a context-aware key encoding module for unified interest key representation; (3) a generative key-based retrieval module to identify diverse and relevant sustained interests; and (4) an adaptive interest update and fusion module to update the interest memory and balance sustained and recent interests. In particular, GIRAM can be seamlessly integrated with existing next POI recommendation models. Experiments on three real-world datasets demonstrate that GIRAM consistently outperforms state-of-the-art methods while maintaining high efficiency in both update time and memory consumption.

Generative recommendation has emerged as a transformative paradigm for capturing the dynamic evolution of user intents in sequential recommendation. While flow-based methods improve the efficiency of diffusion models, they remain hindered by the ''Noise-to-Data'' paradigm, which introduces two critical inefficiencies: prior mismatch, where generation starts from uninformative noise, forcing a lengthy recovery trajectory; and linear redundancy, where iterative solvers waste computation on modeling deterministic preference transitions. To address these limitations, we propose a Flow-based Average Velocity Establishment (Fave) framework for one-step generation recommendation that learns a direct trajectory from an informative prior to the target distribution. Fave is structured via a progressive two-stage training strategy. In Stage 1, we establish a stable preference space through dual-end semantic alignment, applying constraints at both the source (user history) and target (next item) to prevent representation collapse. In Stage 2, we directly resolve the efficiency bottlenecks by introducing a semantic anchor prior, which initializes the flow with a masked embedding from the user's interaction history, providing an informative starting point. Then we learn a global average velocity, consolidating the multi-step trajectory into a single displacement vector, and enforce trajectory straightness via a JVP-based consistency constraint to ensure one-step generation. Extensive experiments on three benchmarks demonstrate that Fave not only achieves state-of-the-art recommendation performance but also delivers an order-of-magnitude improvement in inference efficiency, making it practical for latency-sensitive scenarios. Code is available at https://github.com/Blue130/Fave

Human mobility prediction is a fundamental problem in spatio-temporal data mining with broad applications in urban computing and transportation systems. While large language models (LLMs) have demonstrated strong sequence modeling capabilities, directly adapting them to structured mobility data remains challenging due to long input sequences and efficiency limitations. In this study, we propose ELP-Mob, an efficient framework that reformulates mobility prediction as a language modeling problem. ELP-Mob employs an instruction-style prompt design that incorporates user mobility profiles, historical trajectories, and target future time slots, enabling LLMs to understand mobility patterns and make predictions. To further enhance efficiency, ELP-Mob includes a data selection strategy that reduces redundancy by sampling informative subsets of training users, and a dynamic splitting strategy with token-length control, which scales to long histories while reducing computational overhead. In the GISCUP 2025, ELP-Mob achieved 6th place on the official leaderboard. The source code is publicly available at https://github.com/chwang0721/ELP-Mob.

Single-Source Personalized PageRank (SSPPR) is a fundamental problem in social network analytics, yet maintaining accurate SSPPR query results in evolving social networks poses significant challenges, especially for real-time applications. Existing approaches often overlook the role of subgraphs and struggle with frequent graph updates, resulting in inefficiency regarding dynamic scenarios. In this study, we define a novel personalized PageRank query, n-steps SSPPR, designed to address the challenges of dynamic environments. To support this query, we propose a baseline solution, Pn-FORA, as a foundational approach. While effective, Pn-FORA is inefficient due to its computationally expensive information update scheme. To overcome these limitations, we propose a multithreaded framework for processing massive-scale n-steps SSPPR queries in real-time over evolving graphs. Central to our framework is the Global Walk Synchronization (GWS) method, ensuring the accuracy of SSPPR scores by synchronizing walk information across nodes as the graph evolves. To further enhance GWS, we introduce an influence-aware graph representation to optimize update propagation. Furthermore, we develop a dynamic workload balancing strategy and precision-aware concurrency controls, which achieve an effective balance between efficiency and accuracy. Extensive experiments on real-world datasets demonstrate that our approach significantly outperforms existing methods, offering superior scalability and efficiency for real-time n-steps SSPPR query processing over large-scale social networks. The source code of our implementation is publicly available at https://github.com/SujunShuai/Work2023.

With the proliferation of GPS-enabled devices such as smartphones, the querying of moving objects has attracted substantial attention, with studies covering joins, range and kNN queries, similarity queries, etc. Challenges arise due to variable sampling frequencies, potential inaccuracies in location samples, and the unavailability of locations between samples. Existing similarity joins often rely on discrete location samples, which fail to capture movement uncertainty and may miss meaningful interactions. To address this limitation, we propose Intersection Similarity Join (IS-Join), a novel approach that identifies object pairs based on the overlap of their motion ranges rather than location-based proximity. We define motion ranges as the spatial regions an object may traverse within a given time period, and introduce an intersection similarity measure that quantifies their overlap. To efficiently process IS-Join queries, we develop a Hybrid Ball-tree indexing structure with a repartitioning strategy, enabling scalable candidate filtering. Additionally, we introduce pre-checking and pruning techniques to further reduce computational overhead. Extensive experiments on two real-world trajectory datasets demonstrate that IS-Join significantly outperforms well-designed baselines, achieving up to a 3x reduction in runtime. Our work opens new opportunities for applications such as urban mobility analysis, traffic monitoring, wildlife tracking, and contact tracing.

Point-of-interest (POI) recommendation systems aim to predict the next destinations of user based on their preferences and historical check-ins. Existing generative POI recommendation methods usually employ random numeric IDs for POIs, limiting the ability to model semantic relationships between similar locations. In this paper, we propose Generative Next POI Recommendation with Semantic ID (GNPR-SID), an LLM-based POI recommendation model with a novel semantic POI ID (SID) representation method that enhances the semantic understanding of POI modeling. There are two key components in our GNPR-SID: (1) a Semantic ID Construction module that generates semantically rich POI IDs based on semantic and collaborative features, and (2) a Generative POI Recommendation module that fine-tunes LLMs to predict the next POI using these semantic IDs. By incorporating user interaction patterns and POI semantic features into the semantic ID generation, our method improves the recommendation accuracy and generalization of the model. To construct semantically related SIDs, we propose a POI quantization method based on residual quantized variational autoencoder, which maps POIs into a discrete semantic space. We also propose a diversity loss to ensure that SIDs are uniformly distributed across the semantic space. Extensive experiments on three benchmark datasets demonstrate that GNPR-SID substantially outperforms state-of-the-art methods, achieving up to 16% improvement in recommendation accuracy.

Trajectory representation learning (TRL) maps trajectories to vector embeddings and facilitates tasks such as trajectory classification and similarity search. State-of-the-art (SOTA) TRL methods transform raw GPS trajectories to grid or road trajectories to capture high-level travel semantics, i.e., regions and roads. However, they lose fine-grained spatial-temporal details as multiple GPS points are grouped into a single grid cell or road segment. To tackle this problem, we propose the BLU rred Encoding method, dubbed BLUE, which gradually reduces the precision of GPS coordinates to create hierarchical patches with multiple levels. The low-level patches are small and preserve fine-grained spatial-temporal details, while the high-level patches are large and capture overall travel patterns. To complement different patch levels with each other, our BLUE is an encoder-decoder model with a pyramid structure. At each patch level, a Transformer is used to learn the trajectory embedding at the current level, while pooling prepares inputs for the higher level in the encoder, and up-resolution provides guidance for the lower level in the decoder. BLUE is trained using the trajectory reconstruction task with the MSE loss. We compare BLUE with 8 SOTA TRL methods for 3 downstream tasks, the results show that BLUE consistently achieves higher accuracy than all baselines, outperforming the best-performing baselines by an average of 30.90%. Our code is available at https://github.com/slzhou-xy/BLUE.

Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks. Existing TRL methods use either grid trajectories, capturing movement in free space, or road trajectories, capturing movement in a road network, as input. We observe that the two types of trajectories are complementary, providing either region and location information or providing road structure and movement regularity. Therefore, we propose a novel multimodal TRL method, dubbed GREEN, to jointly utilize Grid and Road trajectory Expressions for Effective representatioN learning. In particular, we transform raw GPS trajectories into both grid and road trajectories and tailor two encoders to capture their respective information. To align the two encoders such that they complement each other, we adopt a contrastive loss to encourage them to produce similar embeddings for the same raw trajectory and design a mask language model (MLM) loss to use grid trajectories to help reconstruct masked road trajectories. To learn the final trajectory representation, a dual-modal interactor is used to fuse the outputs of the two encoders via cross-attention. We compare GREEN with 7 state-of-the-art TRL methods for 3 downstream tasks, finding that GREEN consistently outperforms all baselines and improves the accuracy of the best-performing baseline by an average of 15.99%. Code and data are available at https://github.com/slzhou-xy/GREEN.

Spatio-temporal data captures complex dynamics across both space and time, yet traditional visualizations are complex, require domain expertise and often fail to resonate with broader audiences. Here, we propose MapMuse, a storytelling-based framework for interpreting spatio-temporal datasets, transforming them into compelling, narrative-driven experiences. We utilize large language models and employ retrieval augmented generation (RAG) and agent-based techniques to generate comprehensive stories. Drawing on principles common in cinematic storytelling, we emphasize clarity, emotional connection, and audience-centric design. As a case study, we analyze a dataset of taxi trajectories. Two perspectives are presented: a captivating story based on a heat map that visualizes millions of taxi trip endpoints to uncover urban mobility patterns; and a detailed narrative following a single long taxi journey, enriched with city landmarks and temporal shifts. By portraying locations as characters and movement as plot, we argue that data storytelling drives insight, engagement, and action from spatio-temporal information. The case study illustrates how MapMuse can bridge the gap between data complexity and human understanding. The aim of this short paper is to provide a glimpse to the potential of the cinematic storytelling technique as an effective communication tool for spatio-temporal data, as well as to describe open problems and opportunities for future research.

Trajectory Similarity Join (TS-Join), as a fundamental operation in trajectory data analytics, has been extensively investigated by existing studies in data science. However, existing solutions are almost designed for offline static trajectories, which cannot ensure real-time feedback. In addition, the join results retrieved from existing solutions generally contain a large proportion of outdated similar pairs, making them inapplicable to evolving trajectories. In this light, we study a novel problem of online time-aware trajectory similarity join: Given a stream of evolving trajectories, we aim to dynamically discover trajectory pairs whose spatio-temporal similarity is no less than a specified threshold in a real-time manner. We innovatively introduce a time-aware exponential-decaying similarity function to eliminate outdated results. To support real-time querying over large populations of trajectories, we develop a Parallel Online Trajectory Similarity Join (POTSJ) framework with workload balancing techniques. We further enhance join efficiency through pruning strategies and tailored approximation techniques. The POTSJ framework we propose, which incorporates these elements, is capable of processing online TS-Join while simultaneously satisfying three key objectives: real-time result updates, comprehensive trajectory evaluation, and scalability. Extensive experiments on real-world datasets validate the efficiency and scalability of our POTSJ framework in processing online TS-Join.

Road trajectory generation creates synthetic yet realistic trajectories to tackle data collection costs and privacy concerns. Existing methods generate a trajectory either segment-by-segment using sequence models or holistically in one step using diffusion models. Sequence-based models have good regularity and consistency (i.e., resemble the input trajectories) but lack diversity, while diffusion-based models enhance diversity but sacrifice regularity and consistency. To combine the merits of existing methods, we propose Seed, by bridging sequence and diffusion models for trajectory generation. In particular, Seed adopts a conditional diffusion structure, where a Transformer models the movement of each trajectory along the road segments, and conditioned on the Transformer's output, a diffusion model recovers the next road segment from random noise. The rationale is that the Transformer captures sequential patterns for regularity and consistency, while the diffusion model introduces diversity by recovering from noise. We use a trajectory reconstruction task to train Seed, and design a curriculum learning strategy to accelerate convergence. We compare Seed with 8 state-of-the-art trajectory generation methods on 3 datasets, and the results show that Seed improves the best-performing baseline by over 50%.

Abstract Human beings capable of making and using tools can accomplish tasks far beyond their innate abilities, and this paradigm of integration with tools may not be limited to humans themselves. Recently, the large language model (LLM) has demonstrated immense potential across various fields with its unique planning and reasoning abilities. However, there are still many challenges beyond its capabilities due to deficiencies in its training data and inherent illusions. Thus, integrating LLMs and tools into tool learning agents has become a new emerging research direction. To this end, we present a systematic investigation and comprehensive review of tool-learning agents in this paper. We start by introducing the definition of the tool learning task for Agents and then illustrating the typical architecture of the tool-learning models. Since these tools are all defined by users, LLM does not know what tools there are and what their functions are. Thus, LLMs should first find appropriate tools and split the tool retrieval methods into two categories: training-based and non-training-based. To accurately complete the user task, it is important to decompose the task into several sub-tasks and execute them in the correct order. Following that, we introduce the tool planning methods and organize these works by whether they rely on the model’s inherent reasoning capabilities for planning or utilize external reasoning tools. Due to the rapid development of this field, we also introduce an emerging frontier direction: using multimodal tools for LLM. In addition, we compile current open-source benchmarks and evaluation metrics, focusing on their scale, composition, calculation methods, and assessment dimensions. Next, we introduce several application scenarios for the LLM-based tool learning methods. Finally, we discuss the safety and ethical issues involved in tool learning.

This paper explores explaining session-based recommendation (SR) by path reasoning. Current SR models emphasize accuracy but lack explainability, while traditional path reasoning prioritizes knowledge graph exploration, ignoring sequential patterns present in the session history. Therefore, we propose a generalized hierarchical reinforcement learning framework for SR, which improves the explainability of existing SR models via Path Reasoning, namely PR4SR. Considering the different importance of items to the session, we design the session-level agent to select the items in the session as the starting nodes for path reasoning and the path-level agent to perform path reasoning. In particular, we design a multi-target reward mechanism to adapt to the skip behaviors of sequential patterns in SR and introduce path midpoint reward to enhance the exploration efficiency and accuracy in knowledge graphs. To improve the knowledge graph’s completeness and diversify the paths of explanation, we incorporate extracted feature information from images into the knowledge graph. We instantiate PR4SR in five state-of-the-art SR models (i.e., GRU4REC, NARM, GCSAN, SR-GNN, SASRec) and compare it with other explainable SR frameworks to demonstrate the effectiveness of PR4SR for recommendation and explanation tasks through extensive experiments with these approaches on four datasets.

Next point-of-interest (POI) recommendationpredicts user’s next movement and facilitates location-based applications such as destination suggestion and travel planning. State-of-the-art (SOTA) methods learn an adaptive graph from user trajectories and compute POI representations using graph neural networks (GNNs). However, a single graph cannot capture thediverse dependenciesamong the POIs (e.g., geographical proximity and transition frequency). To tackle this limitation, we propose theAdaptiveGraphContrastiveLearning(AGCL) framework. AGCL constructs multiple adaptive graphs, each modeling a kind of POI dependency and producing one POI representation; and the POI representations from different graphs are merged into amulti-facet representationthat encodes comprehensive information. To train the POI representations, we tailor agraph-based contrastive learning, which encourages the representations of similar POIs to align and dissimilar POIs to differentiate. Moreover, to learn the sequential regularities of user trajectories, we design an attention mechanism to integrate spatial-temporal information into the POI representations. An explicitspatial-temporal biasis also employed to adjust the predictions for enhanced accuracy. We compare AGCL with 10 state-of-the-art baselines on 3 datasets. The results show that AGCL outperforms all baselines and achieves an improvement of 10.14% over the best performing baseline in average accuracy.

Review summarization aims to provide a summary that covers the main aspect of the product review and reflects personal preference. Existing methods employ the historical reviews of customer and product to provide useful clues for the target summary generation. However, most of the existing methods indiscriminately model the historical reviews of customer and product. Since the historicalcustomerreviews provide the personal information while the historicalproductreviews provide the commonly focused aspect of the product, these two types of heterogeneous information should be separately modeled. Moreover, the review rating of the historical reviews can be seen as a high-level abstraction of the customer preference and product which have been ignored by most of the existing methods. In this paper, we propose the Heterogeneous Historical Review aware Review Summarization (HHRRS) which separately models the two types of historical reviews with the rating information by a graph reasoning module with a contrastive loss. We employ a multi-task paradigm that conducts the review sentiment classification and summarization (GRARS) to model the two types of heterogeneous information in a fine-grained manner. We conduct extensive experiments on four benchmark datasets, and demonstrate the superiority of HHRRS on both tasks.

Summarization quality evaluation is a non-trivial task in text summarization. Contemporary methods can be mainly categorized into two scenarios: (1)reference-based:evaluating with human-labeled reference summary; (2)reference-free:evaluating the summary consistency of the document. Recent studies mainly focus on one of these scenarios and explore training neural models to align with human criteria and finally give a numeric score. However, the models from different scenarios are optimized individually, which may result in sub-optimal performance since they neglect the shared knowledge across different scenarios. Besides, designing individual models for each scenario caused inconvenience to the user. Moreover, only providing the numeric quality evaluation score for users cannot help users to improve the summarization model, since they do not know why the score is low. Inspired by this, we proposeUnifiedMulti-scenarioSummarizationEvaluator (UMSE) andMulti-AgentSummarizationEvaluationExplainer (MASEE). More specifically, we propose a perturbed prefix tuning method to share cross-scenario knowledge between scenarios and use a self-supervised training paradigm to optimize the model without extra human labeling. Our UMSE is the first unified summarization evaluation framework engaged with the ability to be used in three evaluation scenarios. We propose a multi-agent summary evaluation explanation method MASEE, which employs several LLM-based agents to generate detailed natural language explanations in four different aspects. Experimental results across three typical scenarios on the benchmark dataset SummEval indicate that our UMSE can achieve comparable performance with several existing strong methods that are specifically designed for each scenario. And intensive quantitative and qualitative experiments also demonstrate the effectiveness of our proposed explanation method, which can generate consistent and accurate explanations.

The next Point-of-interest recommendation has attracted extensive research interest recently, which predicts users' subsequent movements. The main challenge is how to effectively capture users' personalized sequential transitions in check-in trajectory, and various methods have been developed. However, most existing studies ignore the temporal information when conducting the next POI recommendation. To fill this gap, we investigate a time-specific next POI recommendation task, which additionally incorporates the target time information. We propose a brand new Time2Rotation technique to capture the temporal information. Different from conventional methods, we represent timeslots as rotation vectors and then perform the rotation operations. Based on the Time2Rotation technique, we propose a novel rotation-based temporal attention network, namely ROTAN, for the time-specific next POI recommendation task. The ROTAN begins by building a collaborative POI transition graph, capturing the asymmetric temporal influence in sequential transitions. After that, it incorporates temporal information into the modeling of individual check-in trajectories, extracting separate representations for user preference and POI influence to reflect their distinct temporal patterns. Lastly, the target time is integrated to generate recommendations. Extensive experiments are conducted on three real-world datasets, which demonstrates the advantages of the proposed Time2Rotation technique and ROTAN recommendation model.

A trajectory is a sequence of timestamped point locations that captures the movement of an object such as a vehicle. Such trajectories encode complex spatial and temporal patterns and provide rich information about object mobility and the underlying infrastructures, typically road networks, within which the movements occur. A trajectory dataset is evolving when new trajectories are included continuously. The ability to detect anomalous trajectories in online fashion in this setting is fundamental and challenging functionality that has many applications, e.g., location-based services. State-of-the-art solutions determine anomalies based on the shapes or routes of trajectories, ignoring potential anomalies caused by different sampling rates or time offsets. We propose a multi-scale model, termed MST-OATD, for anomalous streaming trajectory detection that considers both the spatial and temporal aspects of trajectories. The model's multi-scale capabilities aim to enable extraction of trajectory features at multiple scales. In addition, to improve model evolvability and to contend with changes in trajectory patterns, the model is equipped with a learned ranking model that updates the training set as new trajectories are included. Experiments on real datasets offer evidence that the model can outperform state-of-the-art solutions and is capable of real-time anomaly detection. Further, the learned ranking model achieves promising results when updating the training set with newly arrived trajectories.

Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems. Existing methods follow two main paradigms to collect evidence: (1) Theretrieve-then-read paradigm retrieves pertinent documents from an external corpus; and (2) thegenerate-then-read paradigm employs large language models (LLMs) to generate relevant documents. However, neither can fully address multifaceted requirements for evidence. To this end, we propose LLMQA, a generalized framework that formulates the ODQA process into three basic steps: query expansion, document selection, and answer generation, combining the superiority of both retrieval-based and generation-based evidence. Since LLMs exhibit their excellent capabilities to accomplish various tasks, we instruct LLMs to play multiple roles as generators, rerankers, and evaluators within our framework, integrating them to collaborate in the ODQA process. Furthermore, we introduce a novel prompt optimization algorithm to refine role-playing prompts and steer LLMs to produce higher-quality evidence and answers. Extensive experimental results on widely used benchmarks (NQ, WebQ, and TriviaQA) demonstrate that LLMQA achieves the best performance in terms of both answer accuracy and evidence quality, showcasing its potential for advancing ODQA research and applications.

Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory classification, and travel-time estimation. However, existing TRL methods often produce vectors that, when used in downstream tasks, yield insufficiently accurate results. A key reason is that they fail to utilize the comprehensive information encompassed by trajectories. We propose a self-supervised TRL framework, called RED, which effectively exploits multiple types of trajectory information. Overall, RED adopts the Transformer as the backbone model and masks the constituting paths in trajectories to train a masked autoencoder (MAE). In particular, RED considers the moving patterns of trajectories by employing a R oad-aware masking strategy that retains key paths of trajectories during masking, thereby preserving crucial information of the trajectories. RED also adopts a spatial-temporal-user joint E mbedding scheme to encode comprehensive information when preparing the trajectories as model inputs. To conduct training, RED adopts D ual-objective task learning : the Transformer encoder predicts the next segment in a trajectory, and the Transformer decoder reconstructs the entire trajectory. RED also considers the spatial-temporal correlations of trajectories by modifying the attention mechanism of the Transformer. We compare RED with 9 state-of-the-art TRL methods for 4 downstream tasks on 3 real-world datasets, finding that RED can usually improve the accuracy of the best-performing baseline by over 5%.

Sequential recommendation is a critical but challenging task in capturing users’ potential preferences due to inherent biases in the data. Existing debiasing recommendation methods aim to eliminate biases from historical interaction data collected by recommender systems and have shown promising results. However, there is another significant bias that hinders the improvement of sequential recommendation models: dynamic item tendency bias. This bias arises because a period might have some unique tendencies consisting of items interacted with by users with the same intent, leading to a dynamic tendency distribution that biases the model training towards these tendencies. To address this issue, we propose a causal approach to model dynamic item tendency bias in sequential recommendation. We first extract tendencies on carefully designed item-item graphs through community detection. We then use causal intervention to conduct deconfounded training to capture true user preferences and introduce the beneficial item tendency bias to the inference process through optimal transport techniques. Experimental results on four real-world datasets demonstrate that our proposed method consistently outperforms state-of-the-art debiasing recommendation methods, confirming that our model is effective in reducing dynamic item tendency bias and dealing with tendency drifts.

Empathy is one of the fundamental abilities of dialog systems. In order to build more intelligent dialogue systems, it’s important to learn how to demonstrate empathy toward others. Existing studies focus on identifying and leveraging the user’s coarse emotion to generate empathetic responses. However, human emotion and dialog act (e.g., intent) evolve as the talk goes along in an empathetic dialogue. This leads to the generated responses with very different intents from the human responses. As a result, empathy failure is ultimately caused. Therefore, using fine-grained emotion and intent sequential data on conversational emotions and dialog act is crucial for empathetic response generation. On the other hand, existing empathy models overvalue the empathy of responses while ignoring contextual relevance, which results in repetitive model-generated responses. To address these issues, we propose a Multi-Factor sequence Fusion framework (EmpMFF) based on conditional variational autoencoder. To generate empathetic responses, the proposed EmpMFF encodes a combination of contextual, emotion, and intent information into a continuous latent variable, which is then fed into the decoder. Experiments on the EmpatheticDialogues benchmark dataset demonstrate that EmpMFF exhibits exceptional performance in both automatic and human evaluations.

Abstract Re-ranking is to refine the candidate ranking list of recommended items, such that the re-ranked list attracts users to purchase or click more items than the candidate one without re-ranking. Items in the candidate list are often ranked by their relevance to users’ interests. It is thus important to exploit the mutual influence between items in the re-ranking process. Existing re-ranking models focus on only the pairwise influence between two items, and have limited capability to exploit the local mutual influence in a group of items. Users often show successive interests on a group of relevant items, e.g., mobile phone, phone covers, wireless headset, namely scene. We propose a novel re-ranking model that jointly exploits the local mutual influence in scenes and the global mutual influence between different scenes. Scene representations are learned by GNN and multi-head attention, where GNN aims to learn local mutual influence while multi-head attention is to learn global mutual influence. To study the interaction between users and scenes, matrix factorization on users is utilized to obtain the user preference, which can be further applied to scenes to compute the scene scores. The final re-ranking list is generated by sorting the predicted scores of all scenes. To further mine user history information and item related user information, we also develop the extension pretraining module which relies on mask mechanism to support users and items high-quality embedding generation. We conduct a comprehensive evaluation on several real-world datasets. The experimental results demonstrate that our model substantially outperforms existing approaches.

Graph embedding, which aims to learn low-dimensional node representations to preserve original graph structures, has attracted extensive research interests. However, most existing graph embedding models represent nodes in Euclidean spaces, which cannot effectively preserve complex patterns, e.g., hierarchical structures. Very recently, several hyperbolic embedding models have been proposed to preserve the hierarchical information in negative curvature spaces. Nevertheless, existing hyperbolic models fail to model the asymmetric proximity between nodes. To address this, we investigate a new asymmetric hyperbolic network representation problem, which targets at jointly preserving the hierarchical structures and asymmetric proximity for general directed graphs. We solve this problem by proposing a novelRotatedLorentzianEmbedding (ROLE) model, which yields two main benefits. First, our model can effectively capture both implicit and explicit hierarchical structures that come from the network topology and category information of nodes, respectively. Second, it can model the asymmetric proximity using rotation transformations. Specifically, we represent each node with a Lorentzian embedding vector, and learn two rotation matrices to reflect the direction of edges. We conduct extensive experiments on four real-world directed graph datasets. Empirical results demonstrate that the proposed approach consistently outperforms various state-of-the-art embedding models. In particular, ROLE achieves HR@1 scores up to 19.8% higher and NDCG@5 scores up to 11.3% higher than the best baselines on the task of node recommendation.

Recent neural architectures in sequence labeling have yielded state-of-the-art performance on single domain data such as newswires. However, they still suffer from (i) requiring massive amounts of training data to avoid overfitting; (ii) huge performance degradation when there is a domain shift in the data distribution between training and testing. In this paper, we investigate the problem of domain adaptation for sequence labeling under homogeneous and heterogeneous settings. We propose MetaSeq, a novel meta-learning approach for domain adaptation in sequence labeling. Specifically, MetaSeq incorporates meta-learning and adversarial training strategies to encourage robust, general and transferable representations for sequence labeling. The key advantage of MetaSeq is that it is capable of adapting to new unseen domains with a small amount of annotated data from those domains. We extensively evaluate MetaSeq on named entity recognition, part-of-speech tagging and slot filling tasks under homogeneous and heterogeneous settings. The experimental results show that MetaSeq achieves state-of-the-art performance against eight baselines. Impressively, MetaSeq surpasses the in-domain performance using only 16.17% and 7% of target domain data on average for homogeneous settings, and 34.76%, 24%, 22.5% of target domain data on average for heterogeneous settings.

The set similarity join finds all pairs of similar sets from two collections of sets. It has many real world applications, such as personalized recommendation and community mining. In this paper, we study the problem of computing the similarity join in a dynamic context, where the sets are updated dynamically. This, however, is inefficient with the state-of-the-art join methods, because they usually assume that data collections are static and have to compute the join result from scratch whenever a set is updated. To address this issue, we propose${{\sf ALJoin}}$, an adaptive filtering approach that computes the join result incrementally based on the update logs. We first investigate the effect of set updates on the similarity values, and on this basis we propose to build a neighborhood index for each set. The neighborhood index of a specific set consists of any other sets that can be transformed into its similar sets within a threshold number of update operations.${{\sf ALJoin}}$then uses this index to effectively identify both similar and dissimilar set pairs based on their update logs. To efficiently build the neighborhood index, we devise several filtering techniques and propose a “lazy-forward” method to reduce the computational cost. In addition, to improve the efficiency on varying workloads, we propose an analytical cost model, and design an online algorithm with performance guarantees to dynamically consolidate the update logs and adapt the neighborhood indexes. We evaluated our method using four real-world datasets. Experimental results show that our approach outperforms existing methods by up to$3.7\times$.

Spatial temporal forecasting plays an important role in improving the quality and performance of Intelligent Transportation Systems. This task is rather challenging due to the complicated and long-range spatial temporal dependencies in traffic network. Existing studies typically employ different deep neural networks to learn the spatial and temporal representations so as to capture the complex and dynamic dependencies. In this paper, we argue that it is insufficient to capture the long-range spatial dependencies from the implicit representations learned by temporal extracting modules. To address this problem, we propose Multi-Step Dependency Relation (MSDR), a brand new variant of recurrent neural network. Instead of only looking at the hidden state from only one latest time step, MSDR explicitly takes those of multiple historical time steps as the input of each time unit. We also develop two strategies to incur the spatial information into the dependency relation embedding between multiple historical time steps and the current one in MSDR. On the basis of it, we propose the Graph-based MSDR (GMSDR) framework to support general spatial temporal forecasting applications by seamlessly integrating graph-based neural networks with MSDR. We evaluate our proposed approach on several popular datasets. The results show that the proposed GMSDR framework outperforms state-of-the-art methods by an obvious margin.

Next Point-of Interest (POI) recommendation plays an important role in location-based applications, which aims to recommend the next POIs to users that they are most likely to visit based on their historical trajectories. Existing methods usually use rich side information, or customized POI graphs to capture the sequential patterns among POIs. However, the graphs only focus on connectivity between POIs. Few studies propose to explicitly learn a weighted POI graph, which could reflect the transition patterns among POIs and show the importance of its different neighbors for each POI. In addition, these approaches simply utilize the user characteristics for personalized POI recommendation without sufficient consideration. To this end, we construct a novel User-POI Knowledge Graph with strong representation ability, called Spatial-Temporal Knowledge Graph (STKG). STKG is used to learn the representations of each node (i.e., user, POI) and each edge. Then, we design a similarity function to construct our POI transition graph based on the learned representations. To incorporate the learned graph into sequential model, we propose a novel network Graph-Flashback for recommendation. Graph-Flashback applies a simplified Graph Convolution Network (GCN) on the POI transition graph to enrich the representation of each POI. Further, we define a similarity function to consider both spatiotemporal information and user preference in modelling sequential regularity. Experimental results on two real-world datasets show that our proposed method achieves the state-of-the-art performance and significantly outperforms all existing solutions.

The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Many studies have shown that geographic information plays an important role in POI recommendation. In this study, we focus on two levels geographic information: local similarity and global similarity. We further show that AUC-MF can be easily extended to incorporate geographical contextual information for POI recommendation. Specifically, we propose two novel methods to incorporate geographical information in AUC-MF. Different from most existing models where the contextual information are incorporated into the objective function, the incorporation of contextual information in AUC-MF is a refinement of the model and a sampling strategy. The sampling strategy could speedup convergence and the refining of recommendations is independent of training of the model. This mechanism also enables AUC-MF to be able produce recommendations refined towards different contextual information, with minimum computational cost. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy.

Text segmentation is a fundamental task in natural language processing. Depending on the levels of granularity, the task can be defined as segmenting a document into topical segments, or segmenting a sentence into elementary discourse units (EDUs). Traditional solutions to the two tasks heavily rely on carefully designed features. The recently proposed neural models do not need manual feature engineering, but they either suffer from sparse boundary tags or cannot efficiently handle the issue of variable size output vocabulary. In light of such limitations, we propose a generic end-to-end segmentation model, namely${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$, which first uses a bidirectional recurrent neural network to encode an input text sequence.${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$then uses another recurrent neural networks, together with a pointer network, to select text boundaries in the input sequence. In this way,${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$does not require any hand-crafted features. More importantly,${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$inherently handles the issue of variable size output vocabulary and the issue of sparse boundary tags. In our experiments,${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$outperforms state-of-the-art models on two tasks: document-level topic segmentation and sentence-level EDU segmentation. As a downstream application, we further propose a hierarchical attention model for sentence-level sentiment analysis based on the outcomes of${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$. The hierarchical model can make full use of both word-level and EDU-level information simultaneously for sentence-level sentiment analysis. In particular, it can effectively exploit EDU-level information, such as the inner properties of EDUs, which cannot be fully encoded in word-level features. Experimental results show that our hierarchical model achieves new state-of-the-art results on the Movie Review and Stanford Sentiment Treebank benchmarks.

Recently, network embedding has attracted extensive research interest. Most existing network embedding models are based on Euclidean spaces. However, Euclidean embedding models cannot effectively capture complex patterns, especially latent hierarchical structures underlying in real-world graphs. Consequently, hyperbolic representation models have been developed to preserve the hierarchical information. Nevertheless, existing hyperbolic models only capture the first-order proximity between nodes. To this end, we propose a new embedding model, named Node2LV, that learns the hyperbolic representations of nodes using squared Lorentzian distances. This yields three advantages. First, our model can effectively capture hierarchical structures that come from the network topology. Second, compared with the conventional hyperbolic embedding methods that use computationally expensive Riemannian gradients, it can be optimized in a more efficient way. Lastly, different from existing hyperbolic embedding models, Node2LV captures higher-order proximities. Specifically, we represent each node with two hyperbolic embeddings, and make the embeddings of related nodes close to each other. To preserve higher-order node proximity, we use a random walk strategy to generate local neighborhood context. We conduct extensive experiments on four different types of real-world networks. Empirical results demonstrate that Node2LV significantly outperforms various graph embedding baselines.

Trajectory similarity computation is an essential operation in many applications of spatial data analysis. In this paper, we study the problem of trajectory similarity computation over spatial network, where the real distances between objects are reflected by the network distance. Unlike previous studies which learn the representation of trajectories in Euclidean space, it requires to capture not only the sequence information of the trajectory but also the structure of spatial network. To this end, we propose GTS, a brand new framework that can jointly learn both factors so as to accurately compute the similarity. It first learns the representation of each point-of-interest (POI) in the road network along with the trajectory information. This is realized by incorporating the distances between POIs and trajectory in the random walk over the spatial network as well as the loss function. Then the trajectory representation is learned by a Graph Neural Network model to identify neighboring POIs within the same trajectory, together with an LSTM model to capture the sequence information in the trajectory. We conduct comprehensive evaluation on several real world datasets. The experimental results demonstrate that our model substantially outperforms all existing approaches.

Aggregate nearest neighbor (Ann) query in both the euclidean space and road networks has been extensively studied, and the flexible aggregate nearest neighbor (Fann) problem further generalizesAnnby introducing an extra flexibility parameter$\phi$that ranges in$(0, 1]$. In this article, we focus onFannon road networks, denoted asFann$_\mathcal {R}$, and its keyword-aware variant, denoted asKFann$_\mathcal {R}$. To solve these problems, we propose a series of universal (i.e., suitable for bothmaxandsum) algorithms, including a Dijkstra-based algorithm that enumerates$P$instead of$\phi |Q|$-combinations of$Q$, a queue-based approach that processes data points from-near-to-far, and a framework that combinesincremental euclidean restriction(IER) and$k$NN. We also propose a specific exact solution tomax-Fann$_\mathcal {R}$and a constant-factor ratio approximate solution tosum-Fann$_\mathcal {R}$. These specific algorithms are easy to implement and can achieve excellent performance in some scenarios. Besides, we further extend this problem to top-$k$and multipleFann$_\mathcal {R}$(resp.,KFann$_\mathcal {R}$) queries. We conduct a comprehensive experimental evaluation for the proposed algorithms on real datasets to demonstrate their superior efficiency and high quality.

Matching similar pairs of trajectories, called trajectory similarity join, is a fundamental functionality in spatial data management. We consider the problem of semantic trajectory similarity join (STS-Join). Each semantic trajectory is a sequence of Points-of-interest (POIs) with both location and text information. Thus, given two sets of semantic trajectories and a threshold θ, the STS-Join returns all pairs of semantic trajectories from the two sets with spatio-textual similarity no less than θ. This join targets applications such as term-based trajectory near-duplicate detection, geo-text data cleaning, personalized ridesharing recommendation, keyword-aware route planning, and travel itinerary recommendation.With these applications in mind, we provide a purposeful definition of spatio-textual similarity. To enable efficient STS-Join processing on large sets of semantic trajectories, we develop trajectory pair filtering techniques and consider the parallel processing capabilities of modern processors. Specifically, we present a two-phase parallel search algorithm. We first group semantic trajectories based on their text information. The algorithm's per-group searches are independent of each other and thus can be performed in parallel. For each group, the trajectories are further partitioned based on the spatial domain. We generate spatial and textual summaries for each trajectory batch, based on which we develop batch filtering and trajectory-batch filtering techniques to prune unqualified trajectory pairs in a batch mode. Additionally, we propose an efficient divide-and-conquer algorithm to derive bounds of spatial similarity and textual similarity between two semantic trajectories, which enable us prune dissimilar trajectory pairs without the need of computing the exact value of spatio-textual similarity. Experimental study with large semantic trajectory data confirms that our algorithm of processing semantic trajectory join is capable of outperforming our well-designed baseline by a factor of 8-12.

Approximate Nearest Neighbor (ANN) search in high-dimensional space is a fundamental task in many applications. Locality-Sensitive Hashing (LSH) is a well-known methodology to solve the ANN problem with theoretical guarantees and empirical performance. We observe that existing LSH-based approaches target at the problem of designing search optimized indexes, which require a number of separate indexes and high index maintenance overhead, and hence impractical for high-dimensional streaming data processing. In this paper, we present PDA-LSH, a novel and practical disk-based LSH index that can offer efficient support for both updates and searches. Experiments on real-world datasets show that our proposal outperforms the state-of-the-art schemes by up to 10× on update performance and up to 2× on search performance.

Recent neural architectures in named entity recognition (NER) have yielded state-of-the-art performance on single domain data such as newswires. However, they still suffer from (i) requiring massive amounts of training data to avoid overfitting; (ii) huge performance degradation when there is a domain shift in the data distribution between training and testing. In this paper, we investigate the problem of domain adaptation for NER under homogeneous and heterogeneous settings. We propose MetaNER, a novel meta-learning approach for domain adaptation in NER. Specifically, MetaNER incorporates meta-learning and adversarial training strategies to encourage robust, general and transferable representations for sequence labeling. The key advantage of MetaNER is that it is capable of adapting to new unseen domains with a small amount of annotated data from those domains. We extensively evaluate MetaNER on multiple datasets under homogeneous and heterogeneous settings. The experimental results show that MetaNER achieves state-of-the-art performance against eight baselines. Impressively, MetaNER surpasses the in-domain performance using only 16.17% and 34.76% of target domain data on average for homogeneous and heterogeneous settings, respectively.

Given a set of server points (e.g., locations) P and a set of client points (e.g., users) O, the problem of maximizing bichromatic reverse k-nearest neighbor (MaxBRkNN) aims to find a region for setting up a new service site such that it can influence the most clients, i.e., it is in the kNN results of most client points. All existing studies first compute the kNN of client points and then perform the MaxBRkNN search. However, computing kNN for all clients is extremely time consuming especially on large datasets. Observing this, we develop an approach which computes kNN for only promising clients by utilising a two-level grid index (ADPGI) to reduce the cost substantially. Empirical studies on both real and synthetic datasets show that our proposed exact algorithm is 3 to 5 times faster than two state-of-the-art MaxBRkNN algorithms.

Geo-textual data that contain spatial, textual, and temporal information are being generated at a very high rate. These geo-textual data cover a wide range of topics. Users may be interested in receiving local popular topics from geo-textual messages. We study the cluster-based subscription matching (CSM) problem. Given a stream of geo-textual messages, we maintain up-to-date clustering results based on a threshold-based online clustering algorithm. Based on the clustering result, we feed subscribers with their preferred geo-textual message clusters according to their specified keywords and location. Moreover, we summarize each cluster by selecting a set of representative messages. The CSM problem considers spatial proximity, textual relevance, and message freshness during the clustering, cluster feeding, and summarization processes. To solve the CSM problem, we propose a novel solution to cluster, feed, and summarize a stream of geo-textual messages efficiently. We evaluate the efficiency of our solution on two real-world datasets and the experimental results demonstrate that our solution is capable of high efficiency compared with baselines.

A set containment join operates on two set-valued attributes with a subset (⊆) relationship as the join condition. It has many real-world applications, such as in publish/subscribe services and inclusion dependency discovery. Existing solutions can be broadly classified into union-oriented and intersection-oriented methods. Based on several recent studies, union-oriented methods are not competitive as they involve an expensive subset enumeration step. Intersection-oriented methods build an inverted index on one attribute and perform inverted list intersection on another attribute. Existing intersection-oriented methods intersect inverted lists one-by-one. In contrast, in this paper, we propose to intersect all the inverted lists simultaneously while skipping many irrelevant entries in the lists. To share computation, we utilize the prefix tree structure and extend our novel list intersection method to operate on the prefix tree. To further improve the efficiency, we propose to partition the data and use different methods to process each partition. We evaluated our methods using both real-world and synthetic datasets. Experimental results show that our approach outperforms existing methods by up to 10×.

The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy.

Identifying influential nodes that can jointly trigger the maximum influence spread in networks is a fundamental problem in many applications such as viral marketing, online advertising, and disease control. Most existing studies assume that social influence is static and they fail to capture the dynamics of influence in reality. In this work, we address the dynamic influence challenge by designing efficient streaming methods that can identify influential nodes from highly dynamic node interaction streams. We first propose a general time-decaying dynamic interaction network (TDN) model to model node interaction streams with the ability to smoothly discard outdated data. Based on the TDN model, we design three algorithms, i.e., SieveADN, BasicReduction and HistApprox. SieveADN identifies influential nodes from a special kind of TDNs with efficiency. BasicReduction uses SieveADN as a basic building block to identify influential nodes from general TDNs. HistApprox significantly improves the efficiency of BasicReduction. More importantly, we theoretically show that all three algorithms enjoy constant factor approximation guarantees. Experiments conducted on various real interaction datasets demonstrate that our approach finds near-optimal solutions with speed at least 5 to 15 times faster than baseline methods.

With the increasing availability of moving-object tracking data, use of this data for route search and recommendation is increasingly important. To this end, we propose a novel parallel split-and-combine approach to enable route search by locations (RSL-Psc). Given a set of routes, a set of places to visit O, and a threshold θ, we retrieve the route composed of sub-routes that (i) has similarity to O no less than θ and (ii) contains the minimum number of sub-route combinations. The resulting functionality targets a broad range of applications, including route planning and recommendation, ridesharing, and location-based services in general. To enable efficient and effective RSL-Psc computation on massive route data, we develop novel search space pruning techniques and enable use of the parallel processing capabilities of modern processors. Specifically, we develop two parallel algorithms, Fully-Split Parallel Search (FSPS) and Group-Split Parallel Search (GSPS). We divide the route split-and-combine task into ∑k=0 M S(|O|,k+1) sub-tasks, where M is the maximum number of combinations and S(⋅) is the Stirling number of the second kind. In each sub-task, we use network expansion and exploit spatial similarity bounds for pruning. The algorithms split candidate routes into sub-routes and combine them to construct new routes. The sub-tasks are independent and are performed in parallel. Extensive experiments with real data offer insight into the performance of the algorithms, indicating that our RSL-Psc problem can generate high-quality results and that the two algorithms are capable of achieving high efficiency and scalability.

Discovering disease-gene association is a fundamental and critical biomedical task, which assists biologists and physicians to discover pathogenic mechanism of syndromes. With various clinical biomarkers measuring the similarities among genes and disease phenotypes, network-based semi-supervised learning (NSSL) has been commonly utilized by these studies to address this class-imbalanced large-scale data issue. However, most existing NSSL approaches are based on linear models and suffer from two major limitations: 1) They implicitly consider a local-structure representation for each candidate; 2) They are unable to capture nonlinear associations between diseases and genes. In this paper, we propose a new framework for disease-gene association task by combining Graph Convolutional Network (GCN) and matrix factorization, named GCN-MF. With the help of GCN, we could capture non-linear interactions and exploit measured similarities. Moreover, we define a margin control loss function to reduce the effect of sparsity. Empirical results demonstrate that the proposed deep learning algorithm outperforms all other state-of-the-art methods on most of metrics.

The matching between trajectories and locations, called Trajectory-to-Location join (TL-Join), is fundamental functionality in spatiotemporal data management. Given a set of trajectories, a set of locations, and a threshold 8, the TL-Join finds all (trajectory, location) pairs from the two sets with spatiotemporal correlation above 8. This join targets diverse applications, including location recommendation, event tracking, and trajectory activity analyses. We address three challenges in relation to the TL-Join: how to define the spatiotemporal correlation between trajectories and locations, how to prune the search space effectively when computing the join, and how to perform the computation in parallel. Specifically, we define new metrics to measure the spatiotemporal correlation between trajectories and locations. We develop a novel parallel collaborative (PCol) search method based on a divide-and-conquer strategy. For each location o, we retrieve the trajectories with high spatiotemporal correlation to o, and then we merge the results. An upper bound on the spatiotemporal correlation and a heuristic scheduling strategy are developed to prune the search space. The trajectory searches from different locations are independent and are performed in parallel, and the result merging cost is independent of the degree of parallelism. Studies of the performance of the developed algorithms using large spatiotemporal data sets are reported.

Aggregate nearest neighbor (ANN) query has been studied in both the Euclidean space and road networks. The flexible aggregate nearest neighbor (FANN) problem further generalizes ANN by introducing an extra flexibility. Given a set of data points P, a set of query points Q, and a user-defined flexibility parameter φ that ranges in (0, 1], an FANN query returns the best candidate from P, which minimizes the aggregate (usually max or sum) distance to any φ |Q| objects in Q. In this paper, we focus on the problem in road networks (denoted as FANNR), and present a series of universal (i.e., suitable for both max and sum) algorithms to answer FANNRqueries in road networks, including a Dijkstra-based algorithm enumerating P, a queue-based approach that processes data points from-near-to-far, and a framework that combines Incremental Euclidean Restriction (IER) and kNN. We also propose a specific exact solution to max-FANNRand a specific approximate solution to sum-FANNRwhich can return a near-optimal result with a guaranteed constant-factor approximation. These specific algorithms are easy to implement and can achieve excellent performance in some scenarios. Besides, we further extend the FANNRto k-FANNR, and successfully adapt most of the proposed algorithms to answer k-FANNRqueries. We conduct a comprehensive experimental evaluation for the proposed algorithms on real road networks to demonstrate their superior efficiency and high quality.

Massive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-to-date top-k most popular terms over a stream of spatio-temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-temporal datasets.

We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in applications such as trip planning and recommendation. To process the TSR query, a set of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.

Personalized PageRank (PPR) is a classic metric that measures the relevance of graph nodes with respect to a source node. Given a graph G, a source node s, and a parameter k, a top-k PPR query returns a set of k nodes with the highest PPR values with respect to s. This type of queries serves as an important building block for numerous applications in web search and social networks, such as Twitter's Who-To-Follow recommendation service. Existing techniques for top-k PPR, however, suffer from two major deficiencies. First, they either incur prohibitive space and time overheads on large graphs, or fail to provide any guarantee on the precision of top-k results (i.e., the results returned might miss a number of actual top-k answers). Second, most of them require significant pre-computation on the input graph G, which renders them unsuitable for graphs with frequent updates (e.g., Twitter's social graph).

We propose and investigate a novel query, the Collective Travel Planning (CTP) query, that finds the lowest-cost route connecting multiple query sources and a destination via at most k meeting points. This type of query is useful in organizing large events, and it can bring significant benefits to society and the environment: it can help optimize the allocation of transportation resources, reduce resource consumption, and enable smarter and greener transportation; and it can help reduce greenhouse-gas emissions and traffic congestion.

The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold θ , the TS-Join returns all pairs of trajectories from the two sets with similarity above θ . This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction. With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.

With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

Knowledge bases have shown their effectiveness in facilitating services like Web search and question-answering. Nevertheless, it remains challenging for ordinary users to fully understand the structure of a knowledge base and to issue structural queries. In many cases, users may have a natural language question and also know some popular (but not all) entities as sample answers. In this paper, we study the Reverse top-k Neighborhood Pattern Query problem, with the aim of discovering structural queries of the question based on: (i) the structure of the knowledge base, and (ii) the sample answers of the question. The proposed solution contains two phases: filter and refine. In the filter phase, a search space of candidate queries is systematically explored. The invalid queries whose result sets do not fully cover the sample answers are filtered out. In the refine phase, all surviving queries are verified to ensure that they are sufficiently relevant to the sample answers, with the assumption that the sample answers are more well-known or popular than other entities in the results of relevant queries. Several optimization techniques are proposed to accelerate the refine phrase. For evaluation, we conduct extensive experiments using the DBpedia knowledge base and a set of real-life questions. Empirical results show that our algorithm is able to provide a small set of possible queries, which contains the query matching the user question in natural language.

Large-scale matrix computation becomes essential for many data data applications, and hence the problem of sketching matrix with small space and high precision has received extensive study for the past few years. This problem is often considered in the row-update streaming model, where the data set is a matrix A -- Rn x d, and the processor receives a row (1 x d) of A at each timestamp. The goal is to maintain a smaller matrix (termed approximation matrix, or simply approximation) B -- Rl x d as an approximation to A, such that the covariance error |AT A - BTB| is small and l ll n.

State-of-the-art trajectory compression methods usually involve high space-time complexity or yield unsatisfactory compression rates, leading to rapid exhaustion of memory, computation, storage, and energy resources. Their ability is commonly limited when operating in a resource-constrained environment especially when the data volume (even when compressed) far exceeds the storage limit. Hence, we propose a novel online framework for error-bounded trajectory compression and ageing called the Amnesic Bounded Quadrant System (ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that includes a normal version (BQS), Fast version (FBQS), and a Progressive version (PBQS). ABQS intelligently manages a given storage and compresses the trajectories with different error tolerances subject to their ages. In the experiments, we conduct comprehensive evaluations for the BQS algorithm family and the ABQS framework. Using empirical GPS traces from flying foxes and cars, and synthetic data from simulation, we demonstrate the effectiveness of the standalone BQS algorithms in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 45 percent). We also show that the operational time of the target resource-constrained hardware platform can be prolonged by up to 41 percent. We then verify that with ABQS, given data volumes that are far greater than storage space, ABQS is able to achieve 15 to 400 times smaller errors than the baselines. We also show that the algorithm is robust to extreme trajectory shapes.

Travel planning and recommendation are important aspects of transportation. We propose and investigate a novel Collective Travel Planning (CTP) query that finds the lowest-cost route connecting multiple sources and a destination, via at most$k$meeting points. When multiple travelers target the same destination (e.g., a stadium or a theater), they may want to assemble at meeting points and then go together to the destination by public transport to reduce their global travel cost (e.g., energy, money, or greenhouse-gas emissions). This type of functionality holds the potential to bring significant benefits to society and the environment, such as reducing energy consumption and greenhouse-gas emissions, enabling smarter and greener transportation, and reducing traffic congestions. The CTP query is Max SNP-hard. To compute the query efficiently, we develop two algorithms, including an exact algorithm and an approximation algorithm. The exact algorithm is capable finding the optimal result for small values of$k$(e.g.,$k = 2$) in interactive time, while the approximation algorithm, which has a$5$-approximation ratio, is suitable for other situations. The performance of the CTP query is studied experimentally with real and synthetic spatial data.

Long-term location tracking, where trajectory compression is commonly used, has gained high interest for many applications in transport, ecology, and wearable computing. However, state-of-the-art compression methods involve high space-time complexity or achieve unsatisfactory compression rate, leading to rapid exhaustion of memory, computation, storage and energy resources. We propose a novel online algorithm for error-bounded trajectory compression called the Bounded Quadrant System (BQS), which compresses trajectories with extremely small costs in space and time using convex-hulls. In this algorithm, we build a virtual coordinate system centered at a start point, and establish a rectangular bounding box as well as two bounding lines in each of its quadrants. In each quadrant, the points to be assessed are bounded by the convex-hull formed by the box and lines. Various compression error-bounds are therefore derived to quickly draw compression decisions without expensive error computations. In addition, we also propose a light version of the BQS version that achieves O(1) complexity in both time and space for processing each point to suit the most constrained computation environments. Furthermore, we briefly demonstrate how this algorithm can be naturally extended to the 3-D case. Using empirical GPS traces from flying foxes, cars and simulation, we demonstrate the effectiveness of our algorithm in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 47%). We then show that with this algorithm, the operational time of the target resource-constrained hardware platform can be prolonged by up to 41%.

Conventional top-k spatial keyword queries require users to explicitly specify their preferences between spatial proximity and keyword relevance. In this work we investigate how to eliminate this requirement by enhancing the conventional queries with interaction, resulting in Interactive Top-k Spatial Keyword (ITkSK) query. Having confirmed the feasibility by theoretical analysis, we propose a three-phase solution focusing on both effectiveness and efficiency. The first phase substantially narrows down the search space for subsequent phases by efficiently retrieving a set of geo-textual k-skyband objects as the initial candidates. In the second phase three practical strategies for selecting a subset of candidates are developed with the aim of maximizing the expected benefit for learning user preferences at each round of interaction. Finally we discuss how to determine the termination condition automatically and estimate the preference based on the user's feedback. Empirical study based on real PoI datasets verifies our theoretical observation that the quality of top-k results in spatial keyword queries can be greatly improved through only a few rounds of interactions.

The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects$O$(e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route$q$, if a cluster$c$has high spatial-object density and is spatially close to$q$, it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scanned from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data.

Different uses of a road network call for the consideration of different travel costs: in route planning, travel time and distance are typically considered, and green house gas (GHG) emissions are increasingly being considered. Further, travel costs such as travel time and GHG emissions are time-dependent and uncertain. To support such uses, we propose techniques that enable the construction of a multi-cost, time-dependent, uncertain graph (MTUG) model of a road network based on GPS data from vehicles that traversed the road network. Based on the MTUG, we define stochastic skyline routes that consider multiple costs and time-dependent uncertainty, and we propose efficient algorithms to retrieve stochastic skyline routes for a given source-destination pair and a start time. Empirical studies with three road networks in Denmark and a substantial GPS data set offer insight into the design properties of the MTUG and the efficiency of the stochastic skyline routing algorithms.

With the increasing availability of human-tracking data (e.g., Public transport IC card data, trajectory data, etc.), human mobility prediction is increasingly important. In this paper, we study a novel problem of using human-tracking data to predict human mobility and to detect over-crowded stations in public transport networks, and then finding unobstructed routes to go around these over-crowded stations. We believe that this study can bring significant benefits to users in many popular mobile applications such as route planning and recommendation, urban computing, and location based services in general. This problem is challenged by two difficulties: (1) how to detect crowded stations effectively, and (2) how to find unobstructed routes in public transport networks efficiently. To overcome these difficulties, we propose three human-mobility prediction methods based on uniform distribution, standard normal distribution, and priority ranking, respectively, to predict human mobility and to detect over-crowded stations. Then, we develop an efficient algorithm based on network expansion to find unobstructed routes in public transport networks. The performance of the developed algorithms has been verified by extensive experiments.

The increasing pervasiveness of location-acquisition technologies has enabled collection of huge amount of trajectories for almost any kind of moving objects. Discovering useful patterns from their movement behaviors can convey valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called gathering, which is a trajectory pattern modeling various group incidents such as celebrations, parades, protests, traffic jams and so on. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. In this work, we first develop a set of novel techniques to tackle the challenge of efficient discovery of gathering patterns on archived trajectory dataset. Afterwards, since trajectory databases are inherently dynamic in many real-world scenarios such as traffic monitoring, fleet management and battlefield surveillance, we further propose an online discovery solution by applying a series of optimization schemes, which can keep track of gathering patterns while new trajectory data arrive. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on a real taxicab trajectory dataset.

This paper addresses the challenges in detecting the potential correlation between numerical data streams, which facilitates the research of data stream mining and pattern discovery. We focus on local correlation with delay, which may occur in burst at different time in different streams, and last for a limited period. The uncertainty on the correlation occurrence and the time delay make it difficult to monitor the correlation online. Furthermore, the conventional correlation measure lacks the ability of reflecting visual linearity, which is more desirable in reality. This paper proposes effective methods to continuously detect the correlation between data streams. Our approach is based on the Discrete Fourier Transform to make rapid cross-correlation calculation with time delay allowed. In addition, we introduce a shape-based similarity measure into the framework, which refines the results by representative trend patterns to enhance the significance of linearity. The similarity of proposed linear representations can quickly estimate the correlation, and the window sliding strategy in segment level improves the efficiency for online detection. The empirical study demonstrates the accuracy of our detection approach, as well as more than $30\%$ improvement of efficiency.

The advances in location positioning and wireless communication technologies have led to a myriad of spatial trajectories representing the mobility of a variety of moving objects. While processing trajectory data with the focus of spatio-temporal features has been widely studied in the last decade, recent proliferation in location-based web applications (e.g., Foursquare, Facebook) has given rise to large amounts of trajectories associated with activity information, called activity trajectory. In this paper, we study the problem of efficient similarity search on activity trajectory database. Given a sequence of query locations, each associated with a set of desired activities, an activity trajectory similarity query (ATSQ) returns k trajectories that cover the query activities and yield the shortest minimum match distance. An order-sensitive activity trajectory similarity query (OATSQ) is also proposed to take into account the order of the query locations. To process the queries efficiently, we firstly develop a novel hybrid grid index, GAT, to organize the trajectory segments and activities hierarchically, which enables us to prune the search space by location proximity and activity containment simultaneously. In addition, we propose algorithms for efficient computation of the minimum match distance and minimum order-sensitive match distance, respectively. The results of our extensive empirical studies based on real online check-in datasets demonstrate that our proposed index and methods are capable of achieving superior performance and good scalability.

The increasing pervasiveness of location-acquisition technologies has enabled collection of huge amount of trajectories for almost any kind of moving objects. Discovering useful patterns from their movement behaviours can convey valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called gathering, which is a trajectory pattern modelling various group incidents such as celebrations, parades, protests, traffic jams and so on. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. Since the process of discovering gathering patterns over large-scale trajectory databases can be quite lengthy, we further develop a set of well thought out techniques to improve the performance. These techniques, including effective indexing structures, fast pattern detection algorithms implemented with bit vectors, and incremental algorithms for handling new trajectory arrivals, collectively constitute an efficient solution for this challenging task. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on a real taxicab trajectory dataset.

Trajectory search plays an important role in various applications such as trip planning and recommendation. However, most existing studies only focus on spatial proximity but ignore individual users' preferences. For example, it is inappropriate to recommend a route containing gravel roads to travelers without off-road vehicles. To accommodate various user preferences, we develop MOIR/UOTS, a trip recommendation system that supports User Oriented Trajectory Search (UOTS) [5], [7] based on our previous MOIR platform [2], [4]. Meanwhile, MOIR/UOTS also functions as a test-bed system for exploring and evaluating novel trajectory searching algorithms. In particular, we demonstrate how ordinary users can interact with MOIR/UOTS to search for trajectories with their preferences, and how MOIR/UOTS helps researchers to evaluate the performance of various algorithms.

Route evaluation and optimization have received significant attention in recent years. In this light, we propose and investigate a novel problem of estimating the travel time (TTE query) for a user specified path by considering the related traffic conditions. Given a query path and a departure time, TTE query finds the estimated travel time along this path. We believe that this type of query may bring important benefits to users in many popular applications, such as travel route evaluation and optimization, and route planning and recommendation. To address the TTE problem in a convincing approach, we construct a traffic-aware spatial network Gpt(V, E) by analysing uncertain trajectory data of moving objects. Based on Gpt(V, E), we define two novel types of TTE queries: TTEepfor an exact query path, and TTElpfor a loose query path. The performance of the construction of traffic-aware spatial network is verified by extensive experiments based on real and synthetic spatial data sets.

Trajectory sharing and searching have received significant attentions in recent years. In this paper, we propose and investigate a novel problem called User Oriented Trajectory Search (UOTS) for trip recommendation. In contrast to conventional trajectory search by locations (spatial domain only), we consider both spatial and textual domains in the new UOTS query. Given a trajectory data set, the query input contains a set of intended places given by the traveler and a set of textual attributes describing the traveler's preference. If a trajectory is connecting/close to the specified query locations, and the textual attributes of the trajectory are similar to the traveler'e preference, it will be recommended to the traveler for reference. This type of queries can bring significant benefits to travelers in many popular applications such as trip planning and recommendation.

Shortest path query is one of the most fundamental queries in spatial network databases. There exist algorithms that can process shortest path queries in real time. However, many complex applications require more than just the calculation of a single shortest path. For example, one of the common ways to determine the importance (or price) of a vertex or an edge in spatial network is to use Vickrey pricing, which intuitively values the vertex v (or edge e ) based on how much harder for travelling from the sources to the destinations without using v (or e ). In such cases, the alternative shortest paths without using v (or e ) are required. In this article, we propose using a precomputation based approach for both single pair alternative shortest path and all pairs shortest paths processing. To compute the alternative shortest path between a source and a destination efficiently, a naïive way is to precompute and store all alternative shortest paths between every pair of vertices avoiding every possible vertex (or edge), which requires O ( n 4 ) space. Currently, the state of the art approach for reducing the storage cost is to choose a subset of the vertices as center points, and only store the single-source alternative shortest paths from those center points. Such approach has the space complexity of O ( n 2 log n ). We propose a storage scheme termed iSPQF , which utilizes shortest path quadtrees by observing the relationships between each avoiding vertex and its corresponding alternative shortest paths. We have reduced the space complexity from the naïive O ( n 4 ) (or the state of the art O ( n 4 log n )) to O (min( γ, L ) n 1.5 ) with comparable query performance of O ( K ), where K is the number of vertices in the returned paths, L is the diameter of the spatial network, and γ is a value that depends on the structure of the spatial network, which is empirically estimated to be 40 for real road networks. Experiments on real road networks have shown that the space cost of the proposed iSPQF is scalable, and both the algorithms based on iSPQF are efficient.

In this paper, we propose and investigate a novel spatial query called Reverse Path Nearest Neighbor (R-PNN) search to find the most accessible locations in road networks. Given a trajectory data-set and a list of location candidates specified by users, if a location o is the Path Nearest Neighbor (PNN) of k trajectories, the influence-factor of o is defined as k and the R-PNN query returns the location with the highest influence-factor. The R-PNN query is an extension of the conventional Reverse Nearest Neighbor (RNN) search. It can be found in many important applications such as urban planning, facility allocation, traffic monitoring, etc. To answer the R-PNN query efficiently, an effective trajectory data pre-processing technique is conducted in the first place. We cluster the trajectories into several groups according to their distribution. Based on the grouped trajectory data, a two-phase solution is applied. First, we specify a tight search range over the trajectory and location data-sets. The efficiency study reveals that our approach defines the minimum search area. Second, a series of optimization techniques are adopted to search the exact PNN for trajectories in the candidate set. By combining the PNN query results, we can retrieve the most accessible locations. The complexity analysis shows that our solution is optimal in terms of time cost. The performance of the proposed R-PNN query processing is verified by extensive experiments based on real and synthetic trajectory data in road networks.

A point detour is a temporary deviation from a user preferred path P (not necessarily a shortest network path) for visiting a data point such as a supermarket or McDonald's. The goodness of a point detour can be measured by the additional traveling introduced, called point detour cost or simply detour cost. Given a preferred path to be traveling on, Best Point Detour (BPD) query aims to identify the point detour with the minimum detour cost. This problem can be frequently found in our daily life but is less studied. In this work, the efficient processing of BPD query is investigated with support of devised optimization techniques. Furthermore, we investigate continuous-BPD query with target at the scenario where the path to be traveling on continuously changes when a user is moving to the destination along the preferred path. The challenge of continuous-BPD query lies in finding a set of update locations which split P into partitions. In the same partition, the user has the same BPD. We process continuous-BPD query by running BPD queries in a deliberately planned strategy. The efficiency study reveals that the number of BPD queries executed is optimal. The efficiency of BPD query and continuous-BPD query processing has been verified by extensive experiments.