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Lisi Chen

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16 papers

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AAAI Conference 2026 Conference Paper

Region-Point Joint Representation for Effective Trajectory Similarity Learning

Hao Long
Silin Zhou
Lisi Chen
Shuo Shang

Recent learning-based methods have reduced the computational complexity of traditional trajectory similarity computation, but state-of-the-art (SOTA) methods still fail to leverage the comprehensive spectrum of trajectory information for similarity modeling. To tackle this problem, we propose RePo, a novel method that jointly encodes Region-wise and Point-wise features to capture both spatial context and fine-grained moving patterns. For region-wise representation, the GPS trajectories are first mapped to grid sequences, and spatial context are captured by structural features and semantic context enriched by visual features. For point-wise representation, three lightweight expert networks extract local, correlation, and continuous movement patterns from dense GPS sequences. Then, a router network adaptively fuses the learned point-wise features, which are subsequently combined with region-wise features using cross-attention to produce the final trajectory embedding. To train RePo, we adopt a contrastive loss with hard negative samples to provide similarity ranking supervision. Experiment results show that RePo achieves an average accuracy improvement of 22.2% over SOTA baselines across all evaluation metrics.

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IJCAI Conference 2025 Conference Paper

Disentangled and Personalized Representation Learning for Next Point-of-Interest Recommendation

Xuan Rao
Shuo Shang
Lisi Chen
Renhe Jiang
Peng Han

Next POInt-of-Interest (POI) recommendation predicts a user's next move and facilitates location-based services such as navigation and travel planning. SOTA methods fuse each POI and its contexts (e. g. , time, category, and region) into a single representation to model sequential user movement. This hinders the effective utilization of context information, and diverse user preferences are also neglected. To tackle these limitations, we propose Disentangled and Personalized Representation Learning (DPRL) as a novel method for next POI recommendation. DPRL decouples POIs and contexts during representation learning, capturing their sequential regularities independently using separate recurrent neural networks (RNNs). To model the preference of each user, DPRL adopts an aggregation mechanism that integrates dynamic user preferences and spatial-temporal factors into the learned representations. We compare DPRL with 16 state-of-the-art baselines. The results show that DPRL outperforms all baselines and achieves an average accuracy improvement of 10. 53% over the best-performing baseline.

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IJCAI Conference 2025 Conference Paper

ST-TAR: An Efficient Spatio-Temporal Learning Framework for Traffic Accident Risk Forecasting

Hongyu Wang
Lisi Chen
Shuo Shang
Peng Han
Christian S. Jensen

Traffic accidents represent a significant concern due to their devastating consequences. The ability to predict future traffic accident risks is of key importance to accident prevention activities in transportation systems. Although existing studies have made substantial efforts to model spatio-temporal correlations, they fall short when it comes to addressing the zero-inflated data issue and capturing spatio-temporal heterogeneity, which reduces their predictive abilities. In addition, improving efficiency is an urgent requirement for traffic accident forecasting. To overcome these limitations, we propose an efficient Spatio-Temporal learning framework for Traffic Accident Risk forecasting (ST-TAR). Taking long-term and short-term data as separate inputs, the ST-TAR model integrates hierarchical multi-view GCN and long short-term cross-attention mechanism to encode spatial dependencies and temporal patterns. We leverage long-term periodicity and short-term proximity for spatio-temporal contrastive learning to capture spatio-temporal heterogeneity. A tailored adaptive risk-level weighted loss function based on efficient locality-sensitive hashing is introduced to alleviate the zero-inflated issue. Extensive experiments on two real-world datasets offer evidence that ST-TAR is capable of advancing state-of-the-art forecasting accuracy with improved efficiency. This makes ST-TAR suitable for applications that require accurate real-time forecasting.

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IJCAI Conference 2025 Conference Paper

TESTN: A Triad-Enhanced Spatio-Temporal Network for Multi-Temporal POI Relationship Inference

Hongyu Wang
Lisi Chen
Shuo Shang

Multi-temporal Point-of-Interest (POI) relationship inference aims to identify evolving relationships among locations over time, providing critical insights for location-based services. While existing studies have made substantial efforts to model relationships with custom-designed graph neural networks, they face the challenge of leveraging POI contextual information characterized by spatial dependencies and temporal dynamics, as well as capturing the heterogeneity of multi-type relationships. To address these challenges, we propose a Triad-Enhanced Spatio-Temporal Network (TESTN), which conceptualizes triads as interactions between relationships for capturing potential interplay. Specifically, TESTN incorporates the spatial 2-hop aggregation layer to capture geographical and semantic information beyond first-order neighbors and the temporal context extractor to integrate relational dynamics within adjacent time segments. Furthermore, we introduce a self-supervised pairwise neighboring relation consistency detection scheme to preserve the heterogeneity of multi-type relationships. Extensive experiments on three real-world datasets demonstrate the superior performance of our TESTN framework.

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AAAI Conference 2024 Conference Paper

KGTS: Contrastive Trajectory Similarity Learning over Prompt Knowledge Graph Embedding

Zhen Chen
Dalin Zhang
Shanshan Feng
Kaixuan Chen
Lisi Chen
Peng Han
Shuo Shang

Trajectory similarity computation serves as a fundamental functionality of various spatial information applications. Although existing deep learning similarity computation methods offer better efficiency and accuracy than non-learning solutions, they are still immature in trajectory embedding and suffer from poor generality and heavy preprocessing for training. Targeting these limitations, we propose a novel framework named KGTS based on knowledge graph grid embedding, prompt trajectory embedding, and unsupervised contrastive learning for improved trajectory similarity computation. Specifically, we first embed map grids with a GRot embedding method to vigorously grasp the neighbouring relations of grids. Then, a prompt trajectory embedding network incorporates the resulting grid embedding and extracts trajectory structure and point order information. It is trained by unsupervised contrastive learning, which not only alleviates the heavy preprocessing burden but also provides exceptional generality with creatively designed strategies for positive sample generation. The prompt trajectory embedding adopts a customized prompt paradigm to mitigate the gap between the grid embedding and the trajectory embedding. Extensive experiments on two real-world trajectory datasets demonstrate the superior performance of KGTS over state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

Heterogeneous Region Embedding with Prompt Learning

Silin Zhou
Dan He
Lisi Chen
Shuo Shang
Peng Han

The prevalence of region-based urban data has opened new possibilities for exploring correlations among regions to improve urban planning and smart-city solutions. Region embedding, which plays a critical role in this endeavor, faces significant challenges related to the varying nature of city data and the effectiveness of downstream applications. In this paper, we propose a novel framework, HREP (Heterogeneous Region Embedding with Prompt learning), which addresses both intra-region and inter-region correlations through two key modules: Heterogeneous Region Embedding (HRE) and prompt learning for different downstream tasks. The HRE module constructs a heterogeneous region graph based on three categories of data, capturing inter-region contexts such as human mobility and geographic neighbors, and intraregion contexts such as POI (Point-of-Interest) information. We use relation-aware graph embedding to learn region and relation embeddings of edge types, and introduce selfattention to capture global correlations among regions. Additionally, we develop an attention-based fusion module to integrate shared information among different types of correlations. To enhance the effectiveness of region embedding in downstream tasks, we incorporate prompt learning, specifically prefix-tuning, which guides the learning of downstream tasks and results in better prediction performance. Our experiment results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

Next POI Recommendation with Dynamic Graph and Explicit Dependency

Feiyu Yin
Yong Liu
Zhiqi Shen
Lisi Chen
Shuo Shang
Peng Han

Next Point-Of-Interest (POI) recommendation plays an important role in various location-based services. Its main objective is to predict the user's next interested POI based on her previous check-in information. Most existing methods directly use users' historical check-in trajectories to construct various graphs to assist sequential models to complete this task. However, as users' check-in data is extremely sparse, it is difficult to capture the potential relations between POIs by directly using these check-in data. To this end, we propose the Sequence-based Neighbour search and Prediction Model (SNPM) for next POI recommendation. In SNPM, the RotatE knowledge graph embedding and Eigenmap methods are used to extract POI relationships implied in check-in data, and build the POI similarity graph. Then, we enhance the model's generalized representations of POIs' general features by aggregating similar POIs. As the context is typically rich and valuable when making Next POI predictions, the sequence model selects which POIs to aggregate not only depends on the current state, but also needs to consider the previous POI sequence. Therefore, we construct a Sequence-based, Dynamic Neighbor Graph (SDNG) to find the similarity neighbourhood and develop a Multi-Step Dependency Prediction model (MSDP) inspired by RotatE, which explicitly leverage information from previous states. We evaluate the proposed model on two real-world datasets, and the experimental results show that the proposed method significantly outperforms existing state-of-the-art POI recommendation methods.

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IJCAI Conference 2022 Conference Paper

Towards Controlling the Transmission of Diseases: Continuous Exposure Discovery over Massive-Scale Moving Objects

Ke Li
Lisi Chen
Shuo Shang
Haiyan Wang
Yang Liu
Panos Kalnis
Bin Yao

Infectious diseases have been recognized as major public health concerns for decades. Close contact discovery is playing an indispensable role in preventing epidemic transmission. In this light, we study the continuous exposure search problem: Given a collection of moving objects and a collection of moving queries, we continuously discover all objects that have been directly and indirectly exposed to at least one query over a period of time. Our problem targets a variety of applications, including but not limited to disease control, epidemic pre-warning, information spreading, and co-movement mining. To answer this problem, we develop an exact group processing algorithm with optimization strategies. Further, we propose an approximate algorithm that substantially improves the efficiency without false dismissal. Extensive experiments offer insight into effectiveness and efficiency of our proposed algorithms.

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IJCAI Conference 2021 Conference Paper

Parallel Subtrajectory Alignment over Massive-Scale Trajectory Data

Lisi Chen
Shuo Shang
Shanshan Feng
Panos Kalnis

We study the problem of subtrajectory alignment over massive-scale trajectory data. Given a collection of trajectories, a subtrajectory alignment query returns new targeted trajectories by splitting and aligning existing trajectories. The resulting functionality targets a range of applications, including trajectory data analysis, route planning and recommendation, ridesharing, and general location-based services. To enable efficient and effective subtrajectory alignment computation, we propose a novel search algorithm and filtering techniques that enable the use of the parallel processing capabilities of modern processors. Experiments with large trajectory datasets are conducted for evaluating the performance of our proposal. The results show that our solution to the subtrajectory alignment problem can generate high-quality results and are capable of achieving high efficiency and scalability.

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AAAI Conference 2021 Conference Paper

Towards Efficient Selection of Activity Trajectories based on Diversity and Coverage

Chengcheng Yang
Lisi Chen
Hao Wang
Shuo Shang

With the prevalence of location based services, activity trajectories are being generated at a rapid pace. The activity trajectory data enriches traditional trajectory data with semantic activities of users, which not only shows where the users have been, but also the preference of users. However, the large volume of data is expensive for people to explore. To address this issue, we study the problem of Diversity-aware Activity Trajectory Selection (DaATS). Given a region of interest for a user, it finds a small number of representative activity trajectories that can provide the user with a broad coverage of different aspects of the region. The problem is challenging in both the efficiency of trajectory similarity computation and subset selection. To tackle the two challenges, we propose a novel solution by: (1) exploiting a deep metric learning method to speedup the similarity computation; and (2) proving that DaATS is an NP-hard problem, and developing an efficient approximation algorithm with performance guarantees. Experiments on two real-world datasets show that our proposal significantly outperforms state-of-the-art baselines.

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IJCAI Conference 2021 Conference Paper

Traffic Congestion Alleviation over Dynamic Road Networks: Continuous Optimal Route Combination for Trip Query Streams

Ke Li
Lisi Chen
Shuo Shang
Panos Kalnis
Bin Yao

Route planning and recommendation have attracted much attention for decades. In this paper, we study a continuous optimal route combination problem: Given a dynamic road network and a stream of trip queries, we continuously find an optimal route combination for each new query batch over the query stream such that the total travel time for all routes is minimized. Each route corresponds to a planning result for a particular trip query in the current query batch. Our problem targets a variety of applications, including traffic-flow management, real-time route planning and continuous congestion prevention. The exact algorithm bears exponential time complexity and is computationally prohibitive for application scenarios in dynamic traffic networks. To address this problem, a self-aware batch processing algorithm is developed in this paper. Extensive experiments offer insight into the accuracy and efficiency of our proposed algorithms.

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AAAI Conference 2020 Conference Paper

Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks

Lisi Chen
Shuo Shang
Bin Yao
Jing Li

Pricing is essential in optimizing transportation resource allocation. Congestion pricing is widely used to reduce urban trafﬁc congestion. We propose and investigate a novel Dynamic Pricing Strategy (DPS) to price travelers’ trips in intelligent transportation platforms (e. g. , DiDi, Lyft, Uber). The trips are charged according to their “congestion contributions” to global urban trafﬁc systems. The dynamic pricing strategy retrieves a matching between n travelers’ trips and the potential travel routes (each trip has k potential routes) to minimize the global trafﬁc congestion. We believe that DPS holds the potential to beneﬁt society and the environment, such as reducing trafﬁc congestion and enabling smarter and greener transportation. The DPS problem is challenging due to its high computation complexity (there exist kn matching possibilities). We develop an efﬁcient and effective approximate matching algorithm based on local search, as well as pruning techniques to further enhance the matching ef- ﬁciency. The accuracy and efﬁciency of the dynamic pricing strategy are veriﬁed by extensive experiments on real datasets.

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AAAI Conference 2020 Conference Paper

Real-Time Route Search by Locations

Lisi Chen
Shuo Shang
Tao Guo

With the proliferation of GPS-based data (e. g. , routes and trajectories), it is of great importance to enable the functionality of real-time route search and recommendations. We deﬁne and study a novel Continuous Route-Search-by-Location (C- RSL) problem to enable real-time route search by locations for a large number of users over route data streams. Given a set of C-RSL queries where each query q contains a set of places q. O to visit and a threshold q. θ, we continuously feed each query q with routes that has similarity to q. O no less than q. θ. We also extend our proposal to support top-k C-RSL problem where each query continuously maintains k most similar routes. The C-RSL problem targets a variety of applications, including real-time route planning, ridesharing, and other location-based services that have real-time demand. To enable efﬁcient route matching on a large number of C- RSL queries, we develop novel parallel route matching algorithms with good time complexity. Extensive experiments with real data offer insight into the performance of our algorithms, indicating that our proposal is capable of achieving high efﬁciency and scalability.

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IJCAI Conference 2020 Conference Paper

Towards Alleviating Traffic Congestion: Optimal Route Planning for Massive-Scale Trips

Ke Li
Lisi Chen
Shuo Shang

We investigate the problem of optimal route planning for massive-scale trips: Given a traffic-aware road network and a set of trip queries Q, we aim to find a route for each trip such that the global travel time cost for all queries in Q is minimized. Our problem is designed for a range of applications such as traffic-flow management, route planning and congestion prevention in rush hours. The exact algorithm bears exponential time complexity and is computationally prohibitive for application scenarios in dynamic traffic networks. To address the challenge, we propose a greedy algorithm and an epsilon-refining algorithm. Extensive experiments offer insight into the accuracy and efficiency of our proposed algorithms.

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AAAI Conference 2019 Conference Paper

Region-Based Message Exploration over Spatio-Temporal Data Streams

Lisi Chen
Shuo Shang

Massive amount of spatio-temporal data that contain location and text content are being generated by location-based social media. These spatio-temporal messages cover a wide range of topics. It is of great significance to discover local trending topics based on users’ location-based and topicbased requirements. We develop a region-based message exploration mechanism that retrieve spatio-temporal message clusters from a stream of spatio-temporal messages based on users’ preferences on message topic and message spatial distribution. Additionally, we propose a region summarization algorithm that finds a subset of representative messages in a cluster to summarize the topics and the spatial attributes of messages in the cluster. We evaluate the efficacy and efficiency of our proposal on two real-world datasets and the results demonstrate that our solution is capable of high efficiency and effectiveness compared with baselines.

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IJCAI Conference 2019 Conference Paper

Toward Efficient Navigation of Massive-Scale Geo-Textual Streams

Chengcheng Yang
Lisi Chen
Shuo Shang
Fan Zhu
Li Liu
Ling Shao

With the popularization of portable devices, numerous applications continuously produce huge streams of geo-tagged textual data, thus posing challenges to index geo-textual streaming data efficiently, which is an important task in both data management and AI applications, e. g. , real-time data streams mining and targeted advertising. This, however, is not possible with the state-of-the-art indexing methods as they focus on search optimizations of static datasets, and have high index maintenance cost. In this paper, we present NQ-tree, which combines new structure designs and self-tuning methods to navigate between update and search efficiency. Our contributions include: (1) the design of multiple stores each with a different emphasis on write-friendness and read-friendness; (2) utilizing data compression techniques to reduce the I/O cost; (3) exploiting both spatial and keyword information to improve the pruning efficiency; (4) proposing an analytical cost model, and using an online self-tuning method to achieve efficient accesses to different workloads. Experiments on two real-world datasets show that NQ-tree outperforms two well designed baselines by up to 10×.

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