Peng Han

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.

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.

AAAI Conference 2025 Conference Paper

SSL-STMFormer Self-Supervised Learning Spatio-Temporal Entanglement Transformer for Traffic Flow Prediction

• Zetao Li
• Zheng Hu
• Peng Han
• Yu Gu
• Shimin Cai

Traffic flow prediction remains a critical issue in intelligent transport systems. Despite significant efforts in traffic flow modeling, existing approaches exhibit several notable limitations: (i) Most models fail to capture traffic flow similarities over long distances and extended periods; (ii) They struggle to account for spatio-temporal heterogeneity induced by varying traffic flow patterns; (iii) Due to their static modeling approach, they struggle to effectively capture the intricate spatio-temporal entanglement. To address these challenges, we propose a traffic flow prediction framework based on self-supervised learning spatio-temporal entanglement transformer(SSL-STMFormer). This framework adopts a self-supervised learning paradigm, leveraging a transformer architecture that captures richer spatio-temporal information to better represent traffic flow patterns. Specifically, a temporal attention module and a spatial attention module are employed to capture the spatio-temporal dependencies of traffic dynamics, respectively, and spatio-temporal entanglement-aware methods are introduced to allow the model to perceive spatio-temporal entanglement and thus better modelling of real traffic environments. Furthermore, to achieve adaptive spatio-temporal self-supervised learning, adaptive data augmentation is applied to the input traffic flow data, and the traffic flow prediction task is enhanced with temporal heterogeneity module and spatial heterogeneity module. Extensive experimental evaluations conducted on six publicly available real-world transportation datasets demonstrate that our method achieves substantial improvements across these datasets.

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.

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.

AAAI Conference 2024 Conference Paper

Prior and Prediction Inverse Kernel Transformer for Single Image Defocus Deblurring

• Peng Tang
• Zhiqiang Xu
• Chunlai Zhou
• Pengfei Wei
• Peng Han
• Xin Cao
• Tobias Lasser

Defocus blur, due to spatially-varying sizes and shapes, is hard to remove. Existing methods either are unable to effectively handle irregular defocus blur or fail to generalize well on other datasets. In this work, we propose a divide-and-conquer approach to tackling this issue, which gives rise to a novel end-to-end deep learning method, called prior-and-prediction inverse kernel transformer (P2IKT), for single image defocus deblurring. Since most defocus blur can be approximated as Gaussian blur or its variants, we construct an inverse Gaussian kernel module in our method to enhance its generalization ability. At the same time, an inverse kernel prediction module is introduced in order to flexibly address the irregular blur that cannot be approximated by Gaussian blur. We further design a scale recurrent transformer, which estimates mixing coefficients for adaptively combining the results from the two modules and runs the scale recurrent ``coarse-to-fine" procedure for progressive defocus deblurring. Extensive experimental results demonstrate that our P2IKT outperforms previous methods in terms of PSNR on multiple defocus deblurring datasets.

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.

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.

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.

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.

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.

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.

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.

IJCAI Conference 2020 Conference Paper

Learning Personalized Itemset Mapping for Cross-Domain Recommendation

• Yinan Zhang
• Yong Liu
• Peng Han
• Chunyan Miao
• Lizhen Cui
• Baoli Li
• Haihong Tang

Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i. e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.

YNICL Journal 2018 Journal Article

Short- and long-range synergism disorders in lifelong premature ejaculation evaluated using the functional connectivity density and network property

• Jiaming Lu
• Xin Zhang
• Huiting Wang
• Zhao Qing
• Peng Han
• Ming Li
• Jiadong Xia
• Fei Chen

IJCAI Conference 2015 Conference Paper

Weakly Supervised Matrix Factorization for Noisily Tagged Image Parsing

• Yulei Niu
• Zhiwu Lu
• Songfang Huang
• Peng Han
• Ji-Rong Wen

In this paper, we propose a Weakly Supervised Matrix Factorization (WSMF) approach to the problem of image parsing with noisy tags, i. e. , segmenting noisily tagged images and then classifying the regions only with image-level labels. Instead of requiring clean but expensive pixel-level labels as strong supervision in the traditional image parsing methods, we take noisy image-level labels as weakly-supervised constraints. Specifically, we first over-segment all the images into multiple regions which are initially labeled based upon the image-level labels. Moreover, from a low-rank matrix factorization viewpoint, we formulate noisily tagged image parsing as a weakly supervised matrix factorization problem. Finally, we develop an efficient algorithm to solve the matrix factorization problem. Experimental results show the promising performance of the proposed WSMF algorithm in comparison with the state-of-the-arts.