Xiangnan He

43.3k total citations · 20 hit papers
275 papers, 17.3k citations indexed

About

Xiangnan He is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Xiangnan He has authored 275 papers receiving a total of 17.3k indexed citations (citations by other indexed papers that have themselves been cited), including 184 papers in Artificial Intelligence, 151 papers in Information Systems and 84 papers in Computer Vision and Pattern Recognition. Recurrent topics in Xiangnan He's work include Recommender Systems and Techniques (148 papers), Advanced Graph Neural Networks (97 papers) and Topic Modeling (76 papers). Xiangnan He is often cited by papers focused on Recommender Systems and Techniques (148 papers), Advanced Graph Neural Networks (97 papers) and Topic Modeling (76 papers). Xiangnan He collaborates with scholars based in China, Singapore and United States. Xiangnan He's co-authors include Tat‐Seng Chua, Xiang Wang, Hanwang Zhang, Yongdong Zhang, Meng Wang, Fuli Feng, Min‐Yen Kan, Yan Li, Liqiang Nie and Yong Li and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Xiangnan He

258 papers receiving 17.0k citations

Hit Papers

LightGCN 2015 2026 2018 2022 2020 2016 2017 2019 2018 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. LightGCN
    2020 2.2k citations Xiangnan He, Xiang Wang et al. profile →
  2. Fast Matrix Factorization for Online Recommendation with Implicit Feedback
    2016 660 citations Xiangnan He, Min‐Yen Kan et al. profile →
  3. Attentive Collaborative Filtering
    2017 571 citations Xiangnan He, Tat‐Seng Chua et al. profile →
  4. Explainable Reasoning over Knowledge Graphs for Recommendation
    2019 455 citations Xiang Wang, Xiangnan He et al. profile →
  5. NAIS: Neural Attentive Item Similarity Model for Recommendation
    2018 394 citations Xiangnan He, Zhenguang Liu et al. IEEE Transactions on Knowledge and Data Engineering profile →
  6. MMGCN
    2019 361 citations Xiang Wang, Xiangnan He et al. profile →
  7. A Simple Convolutional Generative Network for Next Item Recommendation
    2019 353 citations Fajie Yuan, Xiangnan He et al. profile →
  8. Bias and Debias in Recommender System: A Survey and Future Directions
    2022 334 citations Jiawei Chen, Fuli Feng et al. profile →
  9. Attributed Social Network Embedding
    2018 322 citations Xiangnan He, Tat‐Seng Chua et al. IEEE Transactions on Knowledge and Data Engineering profile →
  10. TriRank
    2015 320 citations Xiangnan He, Min‐Yen Kan et al. profile →
  11. A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
    2023 312 citations Xiangnan He et al. profile →
  12. A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
    2022 250 citations Xiangnan He et al. IEEE Transactions on Knowledge and Data Engineering profile →
  13. Deep Item-based Collaborative Filtering for Top-N Recommendation
    2019 247 citations Xiangnan He, Xiang Wang et al. profile →
  14. Causal Intervention for Leveraging Popularity Bias in Recommendation
    2021 239 citations Yang Zhang, Fuli Feng et al. arXiv (Cornell University) profile →
  15. Multi-behavior Recommendation with Graph Convolutional Networks
    2020 234 citations Xiangnan He et al. profile →
  16. Disentangling User Interest and Conformity for Recommendation with Causal Embedding
    2021 200 citations Xiangnan He et al. profile →
  17. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
    2023 155 citations Keqin Bao, Jizhi Zhang et al. profile →
  18. Graph Neural Networks for Recommender System
    2022 149 citations Xiang Wang, Xiangnan He et al. profile →
  19. Diffusion Recommender Model
    2023 97 citations Wenjie Wang, Fuli Feng et al. profile →
  20. CoLLM: Integrating Collaborative Embeddings Into Large Language Models for Recommendation
    2025 21 citations Yang Zhang, Fuli Feng et al. IEEE Transactions on Knowledge and Data Engineering profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Xiangnan He China 68 11.5k 10.8k 5.0k 2.1k 1.5k 275 17.3k
Irwin King Hong Kong 56 7.9k 0.7× 8.0k 0.7× 3.7k 0.7× 1.1k 0.5× 2.8k 1.9× 404 15.4k
Julian McAuley United States 38 7.2k 0.6× 6.3k 0.6× 3.0k 0.6× 1.4k 0.7× 959 0.6× 203 11.5k
Yehuda Koren United States 38 8.6k 0.8× 13.7k 1.3× 5.3k 1.1× 3.2k 1.5× 2.4k 1.6× 69 18.2k
Yong Yu China 54 9.0k 0.8× 4.5k 0.4× 6.2k 1.2× 885 0.4× 1.1k 0.7× 301 16.5k
Tat‐Seng Chua Singapore 84 14.0k 1.2× 7.4k 0.7× 15.3k 3.1× 1.3k 0.6× 1.6k 1.0× 697 28.8k
Ji-Rong Wen China 54 7.0k 0.6× 6.2k 0.6× 2.9k 0.6× 986 0.5× 1.5k 1.0× 425 11.8k
ChengXiang Zhai United States 66 11.4k 1.0× 8.0k 0.7× 2.4k 0.5× 930 0.4× 1.1k 0.7× 386 16.9k
Hongzhi Yin Australia 58 6.0k 0.5× 6.6k 0.6× 1.8k 0.4× 1.1k 0.5× 1.5k 1.0× 308 10.5k
Zhiyuan Liu China 63 15.1k 1.3× 3.3k 0.3× 2.8k 0.6× 1.7k 0.8× 1.1k 0.7× 449 20.2k
Alexander Tuzhilin United States 40 4.2k 0.4× 9.2k 0.9× 2.9k 0.6× 1.7k 0.8× 1.8k 1.2× 158 12.0k

Countries citing papers authored by Xiangnan He

Since Specialization
Citations

This map shows the geographic impact of Xiangnan He's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xiangnan He with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiangnan He more than expected).

Fields of papers citing papers by Xiangnan He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiangnan He. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xiangnan He. The network helps show where Xiangnan He may publish in the future.

Co-authorship network of co-authors of Xiangnan He

This figure shows the co-authorship network connecting the top 25 collaborators of Xiangnan He. A scholar is included among the top collaborators of Xiangnan He based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xiangnan He. Xiangnan He is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Shi, Wei, Sihang Li, Liang Tao, et al.. (2025). Route Sparse Autoencoder to Interpret Large Language Models. 6812–6826.
2.
Zhang, Jizhi, Keqin Bao, Chongming Gao, et al.. (2025). Agentic Feedback Loop Modeling Improves Recommendation and User Simulation. 2235–2244.
3.
Li, Youhua, et al.. (2025). A Content-Driven Micro-Video Recommendation Dataset at Scale. 6486–6491.
4.
Zhang, Yang, et al.. (2024). Text-like Encoding of Collaborative Information in Large Language Models for Recommendation. 9181–9191. 4 indexed citations
5.
Wu, Jiancan, et al.. (2024). LLaRA: Large Language-Recommendation Assistant. 1785–1795. 26 indexed citations
6.
Feng, Fuli, et al.. (2024). Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients. 533–542. 6 indexed citations
7.
Shi, Tianhao, Yang Zhang, Jizhi Zhang, Fuli Feng, & Xiangnan He. (2024). Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach. 448–457.
8.
Feng, Fuli, et al.. (2024). Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation. arXiv (Cornell University). 3253–3264. 1 indexed citations
9.
Sui, Yongduo, et al.. (2024). A Simple Data Augmentation for Graph Classification: A Perspective of Equivariance and Invariance. ACM Transactions on Knowledge Discovery from Data. 19(2). 1–24.
10.
Sui, Yongduo, et al.. (2024). Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning. ACM Transactions on Knowledge Discovery from Data. 18(5). 1–24. 14 indexed citations
11.
Li, Xinglin, Yongduo Sui, Yuan Gao, et al.. (2024). EXGC: Bridging Efficiency and Explainability in Graph Condensation. 721–732. 12 indexed citations
12.
Zhang, Jizhi, Keqin Bao, Yang Zhang, et al.. (2024). Large Language Models for Recommendation: Progresses and Future Directions. 1268–1271. 6 indexed citations
13.
Chen, Jiawei, et al.. (2023). On the Theories Behind Hard Negative Sampling for Recommendation. arXiv (Cornell University). 812–822. 15 indexed citations
14.
Zhang, Jizhi, Keqin Bao, Yang Zhang, et al.. (2023). Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. 993–999. 70 indexed citations
15.
Bao, Keqin, Jizhi Zhang, Yang Zhang, et al.. (2023). TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. 1007–1014. 155 indexed citations breakdown →
16.
Gao, Chongming, Jiawei Chen, Yuan Zhang, et al.. (2023). Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation. arXiv (Cornell University). 238–248. 28 indexed citations
17.
Gao, Yuan, et al.. (2023). Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum. 1528–1538. 60 indexed citations
18.
19.
Tang, Jinhui, Xiaoyu Du, Xiangnan He, et al.. (2019). Adversarial Training Towards Robust Multimedia Recommender System. IEEE Transactions on Knowledge and Data Engineering. 32(5). 855–867. 131 indexed citations
20.
Kan, Min‐Yen, et al.. (2013). Mining Scientific Terms and their Definitions: A Study of the ACL Anthology. 780–790. 22 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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