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Shuo Shang

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28 papers
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28

AAAI Conference 2026 Conference Paper

Beyond Superficial Forgetting: Thorough Unlearning Through Knowledge Density Estimation and Block Re-Insertion

  • Feng Guo
  • Yuntao Wen
  • Shen Gao
  • Junshuo Zhang
  • Shuo Shang

Machine unlearning, which selectively removes harmful knowledge from a pre-trained model without retraining from scratch, is crucial for addressing privacy, regulatory compliance, and ethical concerns in Large Language Models (LLMs). However, existing unlearning methods often struggle to thoroughly remove harmful knowledge, leaving residual harmful knowledge that can be easily recovered. To address these limitations, we propose Knowledge Density-Guided Unlearning via Blocks Reinsertion (KUnBR), a novel approach that first identifies layers with rich harmful knowledge and then thoroughly eliminates the harmful knowledge via re-insertion strategy. Our method introduces knowledge density estimation to quantify and locate layers containing the most harmful knowledge, enabling precise unlearning. Additionally, we design a layer re-insertion strategy that extracts and re-inserts harmful knowledge-rich layers into the original LLM, bypassing gradient obstruction caused by cover layers and ensuring effective gradient propagation during unlearning. Extensive experiments conducted on several unlearning and general capability benchmarks demonstrate that KUnBR achieves state-of-the-art forgetting performance while maintaining model utility.

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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

App2Exa: Accelerating Exact kNN Search via Dynamic Cache-Guided Approximation

  • Ke Li
  • Leong Hou U
  • Shuo Shang

The k-nearest neighbor (kNN) query is a cornerstone of similarity-based applications across various domains. While prior work has enhanced kNN search efficiency, it typically focuses on approximate methods for high-dimensional data or exact methods for low-dimensional data, often assuming static query and data distributions. This creates a significant gap in accelerating exact kNN search for low-to-medium dimensional data with dynamic query distributions. To fill this gap, we propose App2Exa, a cache-guided framework that integrates approximate and exact kNN search. App2Exa utilizes a dynamically maintained cache graph index to retrieve approximate results, which subsequently guide exact search using a VP-Tree with a best-first strategy. A benefit-driven caching mechanism further optimizes performance by prioritizing vectors based on frequency, recency, and computational cost. Experimental results demonstrate that App2Exa significantly boosts efficiency, providing a robust and scalable solution for evolving query patterns and enabling exact kNN search to support higher dimensionality more effectively.

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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

Not All Layers of LLMs Are Necessary During Inference

  • Siqi Fan
  • Xin Jiang
  • Xiang Li
  • Xuying Meng
  • Peng Han
  • Shuo Shang
  • Aixin Sun
  • Yequan Wang

Due to the large number of parameters, the inference phase of Large Language Models (LLMs) is resource-intensive. However, not all requests posed to LLMs are equally difficult to handle. Through analysis, we show that for some tasks, LLMs can achieve results comparable to the final output at some intermediate layers. That is, not all layers of LLMs are necessary during inference. If we can predict at which layer the inferred results match the final results (produced by evaluating all layers), we could significantly reduce the inference cost. To this end, we propose a simple yet effective algorithm named AdaInfer to adaptively terminate the inference process for an input instance. AdaInfer relies on easily obtainable statistical features and classic classifiers like SVM. Experiments on well-known LLMs like the Llama2 series and OPT, show that AdaInfer can achieve an average of 17. 8% pruning ratio, and up to 43% on sentiment tasks, with nearly no performance drop (<1%). Because AdaInfer does not alter LLM parameters, the LLMs incorporated with AdaInfer maintain generalizability across tasks.

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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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NeurIPS Conference 2024 Conference Paper

CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction

  • Shuqi Li
  • Yuebo Sun
  • Yuxin Lin
  • Xin Gao
  • Shuo Shang
  • Rui Yan

There are two issues in news-driven multi-stock movement prediction tasks that are not well solved in the existing works. On the one hand, "relation discovery" is a pivotal part when leveraging the price information of other stocks to achieve accurate stock movement prediction. Given that stock relations are often unidirectional, such as the "supplier-consumer" relationship, causal relations are more appropriate to capture the impact between stocks. On the other hand, there is substantial noise existing in the news data leading to extracting effective information with difficulty. With these two issues in mind, we propose a novel framework called CausalStock for news-driven multi-stock movement prediction, which discovers the temporal causal relations between stocks. We design a lag-dependent temporal causal discovery mechanism to model the temporal causal graph distribution. Then a Functional Causal Model is employed to encapsulate the discovered causal relations and predict the stock movements. Additionally, we propose a Denoised News Encoder by taking advantage of the excellent text evaluation ability of large language models (LLMs) to extract useful information from massive news data. The experiment results show that CausalStock outperforms the strong baselines for both news-driven multi-stock movement prediction and multi-stock movement prediction tasks on six real-world datasets collected from the US, China, Japan, and UK markets. Moreover, getting benefit from the causal relations, CausalStock could offer a clear prediction mechanism with good explainability.

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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

GRLSTM: Trajectory Similarity Computation with Graph-Based Residual LSTM

  • Silin Zhou
  • Jing Li
  • Hao Wang
  • Shuo Shang
  • Peng Han

The computation of trajectory similarity is a crucial task in many spatial data analysis applications. However, existing methods have been designed primarily for trajectories in Euclidean space, which overlooks the fact that real-world trajectories are often generated on road networks. This paper addresses this gap by proposing a novel framework, called GRLSTM (Graph-based Residual LSTM). To jointly capture the properties of trajectories and road networks, the proposed framework incorporates knowledge graph embedding (KGE), graph neural network (GNN), and the residual network into the multi-layer LSTM (Residual-LSTM). Specifically, the framework constructs a point knowledge graph to study the multi-relation of points, as points may belong to both the trajectory and the road network. KGE is introduced to learn point embeddings and relation embeddings to build the point fusion graph, while GNN is used to capture the topology structure information of the point fusion graph. Finally, Residual-LSTM is used to learn the trajectory embeddings.To further enhance the accuracy and robustness of the final trajectory embeddings, we introduce two new neighbor-based point loss functions, namely, graph-based point loss function and trajectory-based point loss function. The GRLSTM is evaluated using two real-world trajectory datasets, and the experimental results demonstrate that GRLSTM outperforms all the state-of-the-art methods significantly.

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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

FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting

  • Xuan Rao
  • Hao Wang
  • Liang Zhang
  • Jing Li
  • Shuo Shang
  • Peng Han

Traffic flow forecasting plays a vital role in the transportation domain. Existing studies usually manually construct correlation graphs and design sophisticated models for learning spatial and temporal features to predict future traffic states. However, manually constructed correlation graphs cannot accurately extract the complex patterns hidden in the traffic data. In addition, it is challenging for the prediction model to fit traffic data due to its irregularly-shaped distribution. To solve the above-mentioned problems, in this paper, we propose a novel learning-based method to learn a spatial-temporal correlation graph, which could make good use of the traffic flow data. Moreover, we propose First-Order Gradient Supervision (FOGS), a novel method for traffic flow forecasting. FOGS utilizes first-order gradients, rather than specific flows, to train prediction model, which effectively avoids the problem of fitting irregularly-shaped distributions. Comprehensive numerical evaluations on four real-world datasets reveal that the proposed methods achieve state-of-the-art performance and significantly outperform the benchmarks.

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AAAI Conference 2022 Conference Paper

GNN-Retro: Retrosynthetic Planning with Graph Neural Networks

  • Peng Han
  • Peilin Zhao
  • Chan Lu
  • Junzhou Huang
  • Jiaxiang Wu
  • Shuo Shang
  • Bin Yao
  • Xiangliang Zhang

Retrosynthetic planning plays an important role in the field of organic chemistry, which could generate a synthetic route for the target product. The synthetic route is a series of reactions which are started from the available molecules. The most challenging problem in the generation of the synthetic route is the large search space of the candidate reactions. Estimating the cost of candidate reactions has been proved effectively to prune the search space, which could achieve a higher accuracy with the same search iteration. And the estimation of one reaction is comprised of the estimations of all its reactants. So, how to estimate the cost of these reactants will directly influence the quality of results. To get a better performance, we propose a new framework, named GNN-Retro, for retrosynthetic planning problem by combining graph neural networks (GNN) and the latest search algorithm. The structure of GNN in our framework could incorporate the information of neighboring molecules, which will improve the estimation accuracy of our framework. The experiments on the USPTO dataset show that our framework could outperform the state-of-the-art methods with a large margin under the same settings.

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IJCAI Conference 2022 Conference Paper

Interactive Information Extraction by Semantic Information Graph

  • Siqi Fan
  • Yequan Wang
  • Jing Li
  • Zheng Zhang
  • Shuo Shang
  • Peng Han

Information extraction (IE) mainly focuses on three highly correlated subtasks, i. e. , entity extraction, relation extraction and event extraction. Recently, there are studies using Abstract Meaning Representation (AMR) to utilize the intrinsic correlations among these three subtasks. AMR based models are capable of building the relationship of arguments. However, they are hard to deal with relations. In addition, the noises of AMR (i. e. , tags unrelated to IE tasks, nodes with unconcerned conception, and edge types with complicated hierarchical structures) disturb the decoding processing of IE. As a result, the decoding processing limited by the AMR cannot be worked effectively. To overcome the shortages, we propose an Interactive Information Extraction (InterIE) model based on a novel Semantic Information Graph (SIG). SIG can guide our InterIE model to tackle the three subtasks jointly. Furthermore, the well-designed SIG without noise is capable of enriching entity and event trigger representation, and capturing the edge connection between the information types. Experimental results show that our InterIE achieves state-of-the-art performance on all IE subtasks on the benchmark dataset (i. e. , ACE05-E+ and ACE05-E). More importantly, the proposed model is not sensitive to the decoding order, which goes beyond the limitations of AMR based 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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IJCAI Conference 2020 Conference Paper

Contextualized Point-of-Interest Recommendation

  • Peng Han
  • Zhongxiao Li
  • Yong Liu
  • Peilin Zhao
  • Jing Li
  • Hao Wang
  • Shuo Shang

Point-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i. e. , global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.

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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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IJCAI Conference 2019 Conference Paper

Adversarial Transfer for Named Entity Boundary Detection with Pointer Networks

  • Jing Li
  • Deheng Ye
  • Shuo Shang

In this paper, we focus on named entity boundary detection, which aims to detect the start and end boundaries of an entity mention in text, without predicting its type. A more accurate and robust detection approach is desired to alleviate error propagation in downstream applications, such as entity linking and fine-grained typing systems. Here, we first develop a novel entity boundary labeling approach with pointer networks, where the output dictionary size depends on the input, which is variable. Furthermore, we propose AT-Bdry, which incorporates adversarial transfer learning into an end-to-end sequence labeling model to encourage domain-invariant representations. More importantly, AT-Bdry can reduce domain difference in data distributions between the source and target domains, via an unsupervised transfer learning approach (i. e. , no annotated target-domain data is necessary). We conduct Formal Text to Formal Text, Formal Text to Informal Text and ablation evaluations on five benchmark datasets. Experimental results show that AT-Bdry achieves state-of-the-art transferring performance against recent baselines.

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AAAI Conference 2019 Conference Paper

Preference-Aware Task Assignment in Spatial Crowdsourcing

  • Yan Zhao
  • Jinfu Xia
  • Guanfeng Liu
  • Han Su
  • Defu Lian
  • Shuo Shang
  • Kai Zheng

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment. We model worker preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover worker preferences for different categories of tasks in different time slots. Several preference-aware task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.

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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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AAAI Conference 2019 Conference Paper

Submodular Optimization over Streams with Inhomogeneous Decays

  • Junzhou Zhao
  • Shuo Shang
  • Pinghui Wang
  • John C.S. Lui
  • Xiangliang Zhang

Cardinality constrained submodular function maximization, which aims to select a subset of size at most k to maximize a monotone submodular utility function, is the key in many data mining and machine learning applications such as data summarization and maximum coverage problems. When data is given as a stream, streaming submodular optimization (SSO) techniques are desired. Existing SSO techniques can only apply to insertion-only streams where each element has an infinite lifespan, and sliding-window streams where each element has a same lifespan (i. e. , window size). However, elements in some data streams may have arbitrary different lifespans, and this requires addressing SSO over streams with inhomogeneous-decays (SSO-ID). This work formulates the SSO-ID problem and presents three algorithms: BASIC- STREAMING is a basic streaming algorithm that achieves an (1/2 − ) approximation factor; HISTAPPROX improves the efficiency significantly and achieves an (1/3 − ) approximation factor; HISTSTREAMING is a streaming version of HISTAPPROX and uses heuristics to further improve the efficiency. Experiments conducted on real data demonstrate that HISTSTREAMING can find high quality solutions and is up to two orders of magnitude faster than the naive GREEDY algorithm.

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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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