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Computer Science
China
2026

D-Index & Metrics

Computer Science

D-Index
93
Citations
50727
World Ranking
505
National Ranking
69

Research.com Recognitions

  • 2026 - Research.com Computer Science in China Leader Award
  • 2025 - Research.com Computer Science in China Leader Award

Overview

Xiangnan He is affiliated with the University of Science and Technology of China. Their research primarily focuses on the field of Computer Science, with significant contributions in several subfields including Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Management Science and Operations Research, and Molecular Biology.

The scientist's research covers a range of main topics, emphasizing Recommender Systems and Techniques, Advanced Graph Neural Networks, Topic Modeling, Advanced Bandit Algorithms Research, Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, and Complex Network Analysis Techniques.

Their recent scholarly papers include the following:

  • Bias and Debias in Recommender System: A Survey and Future Directions (2022, ACM Transactions on Information Systems)
  • A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2023, ACM Transactions on Recommender Systems)
  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (2020, arXiv (Cornell University))
  • A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation (2022, IEEE Transactions on Knowledge and Data Engineering)
  • Graph Neural Networks for Recommender System (2022, Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining)

Xiangnan He frequently collaborates with other researchers, with notable coauthors including Fuli Feng, Xiang Wang, Tat-Seng Chua, Jiancan Wu, and Jiawei Chen.

The scientist publishes extensively in various venues such as arXiv (Cornell University), IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, Proceedings of the 30th ACM International Conference on Multimedia, and Proceedings of the AAAI Conference on Artificial Intelligence.

Best Publications

  • Neural Collaborative Filtering

    Xiangnan He;Lizi Liao;Hanwang Zhang;Liqiang Nie

  • Neural Graph Collaborative Filtering

    Xiang Wang;Xiangnan He;Meng Wang;Fuli Feng

  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

    Xiangnan He;Kuan Deng;Xiang Wang;Yan Li

  • KGAT: Knowledge Graph Attention Network for Recommendation

    Xiang Wang;Xiangnan He;Yixin Cao;Meng Liu

  • Neural Factorization Machines for Sparse Predictive Analytics

    Xiangnan He;Tat-Seng Chua

  • Fast Matrix Factorization for Online Recommendation with Implicit Feedback

    Xiangnan He;Hanwang Zhang;Min-Yen Kan;Tat-Seng Chua

  • Disentangled Graph Collaborative Filtering

    Xiang Wang;Hongye Jin;An Zhang;Xiangnan He

  • Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

    Jun Xiao;Hao Ye;Xiangnan He;Hanwang Zhang

  • Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

    Jingyuan Chen;Hanwang Zhang;Xiangnan He;Liqiang Nie

  • Explainable Reasoning over Knowledge Graphs for Recommendation

    Xiang Wang;Dingxian Wang;Canran Xu;Xiangnan He

  • Temporal Relational Ranking for Stock Prediction

    Fuli Feng;Xiangnan He;Xiang Wang;Cheng Luo

  • A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

    Unknown

  • Bias and Debias in Recommender System: A Survey and Future Directions

    Jiawei Chen;Hande Dong;Xiang Wang;Fuli Feng

  • Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

    Yixin Cao;Xiang Wang;Xiangnan He;Zikun Hu

  • A Simple Convolutional Generative Network for Next Item Recommendation

    Fajie Yuan;Alexandros Karatzoglou;Ioannis Arapakis;Joemon M. Jose

  • MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video

    Yinwei Wei;Xiang Wang;Liqiang Nie;Xiangnan He

  • NAIS: Neural Attentive Item Similarity Model for Recommendation

    Xiangnan He;Zhankui He;Jingkuan Song;Zhenguang Liu

  • TriRank: Review-aware Explainable Recommendation by Modeling Aspects

    Xiangnan He;Tao Chen;Min-Yen Kan;Xiao Chen

  • Learning Intents behind Interactions with Knowledge Graph for Recommendation

    Xiang Wang;Tinglin Huang;Dingxian Wang;Yancheng Yuan

  • A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation

    Unknown

  • Attributed Social Network Embedding

    Lizi Liao;Xiangnan He;Hanwang Zhang;Tat-Seng Chua

  • Causal Intervention for Leveraging Popularity Bias in Recommendation

    Yang Zhang;Fuli Feng;Xiangnan He;Tianxin Wei

  • Video Question Answering via Gradually Refined Attention over Appearance and Motion

    Dejing Xu;Zhou Zhao;Jun Xiao;Fei Wu

  • Adversarial Personalized Ranking for Recommendation

    Xiangnan He;Zhankui He;Xiaoyu Du;Tat-Seng Chua

Frequent Co-Authors

Tat-Seng Chua
Tat-Seng Chua National University of Singapore
Fuli Feng
Fuli Feng University of Science and Technology of China
Hanwang Zhang
Hanwang Zhang Nanyang Technological University
Liqiang Nie
Liqiang Nie Shandong University
Yong Li
Yong Li Tsinghua University
Yongdong Zhang
Yongdong Zhang University of Science and Technology of China
Min-Yen Kan
Min-Yen Kan National University of Singapore
Richang Hong
Richang Hong Hefei University of Technology
Meng Wang
Meng Wang Hefei University of Technology
Depeng Jin
Depeng Jin Tsinghua University

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