Senzhang Wang

Venues

Frequent coauthors

Papers

1. DiMA: Distinguishing Resident and Tourist Preferences via Multi-Modal LLM Alignment for Out-of-Town Cross-Domain Recommendation
   AAAI 2026 ·

   Fan Zhang

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2. Same Last-Item Confusion Unveiled: A Unified Mitigation Framework for Graph Learning in Session-Based Recommendation
   WWW 2026 ·

   Jinpeng Chen

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3. Beyond Text: Fine-Grained Multi-Modal Fact Verification with Hypergraph Transformers
   AAAI 2025 ·

   Hui Pang

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4. C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Prediction
   AAAI 2025 ·

   Zichen Wang

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5. FairTP: A Prolonged Fairness Framework for Traffic Prediction
   AAAI 2025 ·

   Jiangnan Xia

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6. Ghidorah: Towards Robust Multi-Scale Information Diffusion Prediction via Test-Time Training
   AAAI 2025 ·

   Wenting Zhu

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7. Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
   SIGIR 2025 ·

   Jinpeng Chen

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8. Large Language Model Simulator for Cold-Start Recommendation
   WSDM 2025 ·

   Feiran Huang

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9. Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendation
   ACM MM 2025 ·

   Fan Zhang

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10. Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation
    WSDM 2025 ·

    Chenyuan Wu

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11. MRR-FV: Unlocking Complex Fact Verification with Multi-Hop Retrieval and Reasoning
    AAAI 2025 ·

    Liwen Zheng

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12. Mixture of Semantic and Spatial Experts for Explainable Traffic Prediction
    CIKM 2025 ·

    Yang Hu

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13. MoKGNN: Boosting Graph Neural Networks via Mixture of Generic and Task-Specific Language Models
    WSDM 2025 ·

    Hao Yan

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14. Role-aware Multi-agent Reinforcement Learning for Coordinated Emergency Traffic Control
    NeurIPS 2025 ·

    Ming Cheng

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15. STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
    IJCAI 2025 ·

    Yiming Wang

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16. STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
    CIKM 2025 ·

    Zhenye Yang

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17. Temporal Blocks with Memory Replay for Dynamic Graph Representation Learning
    CIKM 2025 ·

    Zhigang Yu

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18. Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism
    WWW 2025 ·

    Jun Yin

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19. When Graph Meets Multimodal: Benchmarking and Meditating on Multimodal Attributed Graph Learning
    SIGKDD 2025 ·

    Hao Yan

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20. AdaTM: Fine-grained Urban Flow Inference with Adaptive Knowledge Transfer across Multiple Cities
    CIKM 2024 ·

    Yuhao Zheng

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21. FGITrans: Cross-City Transformer for Fine-grained Urban Flow Inference
    CIKM 2024 ·

    Yuhao Zheng

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22. Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination
    NeurIPS 2024 ·

    Ruochen Liu

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23. FuseAnyPart: Diffusion-Driven Facial Parts Swapping via Multiple Reference Images
    NeurIPS 2024 ·

    Zheng Yu

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24. GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
    SIGIR 2024 ·

    Peiyan Zhang

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25. High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text Attributed Graphs
    WWW 2024 ·

    Peiyan Zhang

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26. Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
    WWW 2024 ·

    Hao Chen

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27. Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
    SIGKDD 2024 ·

    Yijie Zhang

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28. MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization
    WSDM 2024 ·

    Dongcheng Zou

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29. PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
    SIGKDD 2024 ·

    Ronghui Xu

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30. SaSDim: Self-Adaptive Noise Scaling Diffusion Model for Spatial Time Series Imputation
    IJCAI 2024 ·

    Shunyang Zhang

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31. Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
    IJCAI 2024 ·

    Shunyang Zhang

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32. TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
    SIGIR 2024 ·

    Peiyan Zhang

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