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Database Systems for Advanced Applications

30th International Conference, DASFAA 2025, Singapore, Singapore, May 26–29, 2025, Proceedings, Part V

  • Conference proceedings
  • © 2026

Overview

Editors:
  1. Feida Zhu

    1. Singapore Management University, Singapore, Singapore

  2. Philip S. Yu

    1. University of Illinois, Chicago, USA

  3. Akiyo Nadamoto

    1. Konan University, Kobe, Japan

  4. Ee-peng Lim

    1. Singapore Management University, Singapore, Singapore

  5. Kyuseok Shim

    1. Seoul National University, Seoul, Korea (Republic of)

  6. Wei Ding

    1. University of Massachusetts Amherst, Amherst, USA

  7. Bingxue Zhang

    1. University of Shanghai for Science and Technology, Shanghai, China

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15990)

Included in the following conference series:

Conference proceedings info: DASFAA 2025.

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About this book

This six-volume set LNCS 15986-15991 constitutes the proceedings of the 30th International Conference on Database Systems for Advanced Applications, DASFAA 2025, held in Singapore, during May 26–29, 2025.
The 136 full papers presented in this book together with 89 short papers were carefully reviewed and selected from 731 submissions. They cover topics such as

Part I- Machine Learning and Text.
Part II- Emerging Application; NLP and Spatial-Temporal.
Part III- Graph; Knowledge Graph.
Part V- Recommendation and Security & Privacy.
Part VI- Language Model; Industry Papers and Demo Papers.

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Table of contents (44 papers)

  1. Front Matter

    Pages i-xxix
    Download chapter PDF

  2. Recommendation

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Hypergraph Enhanced Knowledge Tree Prompt Learning for Next-Basket Recommendation

      • Zifeng Mai, Jianyang Zhai, Naiqing Li, Chang-Dong Wang, Zhongjie Zeng, Ya Li et al.
      Pages 3-18

    3. MHGCP: Multi-view Heterogeneous Graph with Cross-View Projection for Recommendation

      • Xinlong Feng, Qianfang Xu, Shaojie Tang, Bo Xiao
      Pages 19-34

    4. Towards Scenario-Adaptive User Behavior Modeling for Multi-scenario Recommendation

      • Jin Huang, Yu Qian, Zixu Yang, Zhijun Sun
      Pages 35-50

    5. Leave no one Behind: Enhancing Diversity While Maintaining Accuracy in Social Recommendation

      • Lei Li, Xiao Zhou
      Pages 51-67

    6. Counterfactual Path Augmentation for Reinforcement Reasoning in Explainable Recommendation

      • Yue Kou, Eryu Jiang, Derong Shen, Xiangmin Zhou, Dong Li, Tiezheng Nie et al.
      Pages 68-83

    7. Adaptive Personalized Federated Recommendation with Global Knowledge Distillation

      • Jianzhe Zhao, Lingyan He, Fanzhe Lin, Jiaqi Ding, Xiaxue Zhu, Guibing Guo
      Pages 84-99

    8. FHCF: Fully-Hyperbolic Symmetric Graph Learning for Collaborative Filtering

      • Guanghui Zhu, Wang Lu, Chunfeng Yuan, Yihua Huang
      Pages 100-116

    9. UGDA: A Unified Graph-Based Method with Domain-Specific Adaptation for Multi-Domain Recommendation

      • Bin Ruan, Hao Liu, Yitian Tu, Zhiying Deng, Zhiqiang Guo, Jianjun Li
      Pages 117-133

    10. Self-supervised Hierarchical Representation for Medication Recommendation

      • Yuliang Liang, Yuting Liu, Yizhou Dang, Enneng Yang, Guibing Guo, Wei Cai et al.
      Pages 134-150

    11. Self-supervised Dual Graph and Intention Association for Session-Based Recommendation

      • Junnan Zhuo, Bohan Li, Sujie Yu, Shuai Xu, Xinzhe Zhao, Zekun Xu et al.
      Pages 151-167

    12. Exercise Recommendation Based on Feature-Aligned Knowledge Tracing

      • Wei Ji, Zhiyu Chen, Jing Xiao
      Pages 168-184

    13. Joint User and Item Prototype Alignment for Cross-Platform Recommendation

      • Kehan Chen, Jiaxing Bai, Yishan Ji, Chen Lin, Ying Wang
      Pages 185-201

    14. Diffusion Multi-behavior Recommender Model

      • Anchen Li, Jinglong Ji, Riting Xia, Bo Yang
      Pages 202-217

    15. HHGCN-DrugRec: Hierarchical HyperGraph Convolution Network for Drug Combination Recommendation

      • Zihan Zhang, Hongzhi Liu, Tianqi Sun, Xiaoshuang Guo, Zhonghai Wu
      Pages 218-234

    16. Emotion-Based Conversational Recommendation by Inferring Implicit Users’ Preferences from Their Subjective Claims

      • Xuanming Zhang, Yonghe Lu, Jianxing Yu, Huaijie Zhu, Wei Liu, Wenqing Chen et al.
      Pages 235-252

    17. CDIVR: Cognitive Dissonance-Aware Interactive Video Recommendation

      • Sicong Liu, Ke Yan, Haojie Shi, Ming Jia
      Pages 253-268

    18. Modeling Personalized Short-Term and Periodic Long-Term Preferences for Enhanced Next POI Recommendation

      • Mo Li, Zhaosong Zhao, Linlin Ding, Mingyang Ma
      Pages 269-285

    19. DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-Level

      • Yifan Wang, Shen Gao, Jiabao Fang, Lisi Chen, Peng Han, Shuo Shang
      Pages 286-302
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Other volumes

  1. Database Systems for Advanced Applications

  2. Database Systems for Advanced Applications

  3. Database Systems for Advanced Applications

  4. Database Systems for Advanced Applications

Editors and Affiliations

  • Singapore Management University, Singapore, Singapore

    Feida Zhu, Ee-peng Lim

  • University of Illinois, Chicago, USA

    Philip S. Yu

  • Konan University, Kobe, Japan

    Akiyo Nadamoto

  • Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  • University of Massachusetts Amherst, Amherst, USA

    Wei Ding

  • University of Shanghai for Science and Technology, Shanghai, China

    Bingxue Zhang

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