Xiangnan He

43.3k citations

275 papers · 17.3k indexed · 20 hit papers · h-index 68

Information Systems top 0.01%
- Recommender Systems and Techniques 148
Artificial Intelligence top 0.02%
- Advanced Graph Neural Networks 97
- Topic Modeling 76
Computer Vision and Pattern Recognition top 0.1%
- Image Retrieval and Classification Techniques 25
- Multimodal Machine Learning Applications 24
- Advanced Image and Video Retrieval Techniques 20
Management Science and Operations Research top 0.1%
- Advanced Bandit Algorithms Research 35
Transportation top 0.5%
Statistical and Nonlinear Physics
- Complex Network Analysis Techniques 18

Co-authors: Tat‐Seng Chua Xiang Wang Hanwang Zhang Yongdong Zhang Meng Wang Fuli Feng Min‐Yen Kan Yan Li
Cited by: Information Systems Artificial Intelligence Computer Vision and Pattern Recognition
Partner nations: China Singapore United States

In The Last Decade

Xiangnan He

258 papers receiving 17.0k citations

Hit Papers

15 papers align trajectories log scale

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.

2025 CoLLM: Integrating Collaborative Embeddings Into Large Language Models for Recommendation
2023 Diffusion Recommender Model
2023 A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
2023 TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
2022 A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
2022 Graph Neural Networks for Recommender System
2022 Bias and Debias in Recommender System: A Survey and Future Directions
2021 Disentangling User Interest and Conformity for Recommendation with Causal Embedding
2021 Causal Intervention for Leveraging Popularity Bias in Recommendation
2020 Multi-behavior Recommendation with Graph Convolutional Networks
2020 LightGCN
2019 Explainable Reasoning over Knowledge Graphs for Recommendation
2019 A Simple Convolutional Generative Network for Next Item Recommendation
2019 Deep Item-based Collaborative Filtering for Top-N Recommendation
2019 MMGCN
2018 NAIS: Neural Attentive Item Similarity Model for Recommendation
2018 Attributed Social Network Embedding
2017 Attentive Collaborative Filtering
2016 Fast Matrix Factorization for Online Recommendation with Implicit Feedback
2015 TriRank

Peers

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

The 25 scholars most cited alongside Xiangnan He, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Xiangnan He Line = papers co-authored together Xiangnan He links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work
1	Route Sparse Autoencoder to Interpret Large Language Models Wei Shi,Sihang Li,Liang Tao,Mingyang Wan,Guojun Ma,Xiang Wang,Xiangnan He	2025	0
2	Agentic Feedback Loop Modeling Improves Recommendation and User Simulation Shihao Cai,Jizhi Zhang,Keqin Bao,Chongming Gao,Qifan Wang,Fuli Feng,Xiangnan He	2025	0
3	A Content-Driven Micro-Video Recommendation Dataset at Scale Yongxin Ni,Yu Cheng,Xiangyan Liu,Junchen Fu,Youhua Li,Xiangnan He,Yongfeng Zhang,Fajie Yuan	2025	0
4	Text-like Encoding of Collaborative Information in Large Language Models for Recommendation Yang Zhang,Keqin Bao,Ming Yan,Wenjie Wang,Fuli Feng,Xiangnan He	2024	4
5	LLaRA: Large Language-Recommendation Assistant Jiayi Liao,Sihang Li,Zhengyi Yang,Jiancan Wu,Yancheng Yuan,Xiang Wang,Xiangnan He	2024	26
6	Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients Zihao Zhao,Yi Jing,Fuli Feng,Jiancan Wu,Chongming Gao,Xiangnan He	2024	6
7	Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach Tianhao Shi,Yang Zhang,Jizhi Zhang,Fuli Feng,Xiangnan He	2024	0
8	Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation Wentao Shi,Chenxu Wang,Fuli Feng,Yang Zhang,Wenjie Wang,Junkang Wu,Xiangnan He	2024	1
9	A Simple Data Augmentation for Graph Classification: A Perspective of Equivariance and Invariance Yongduo Sui,Shuyao Wang,Jie Sun,Zhiyuan Liu,Qing Cui,Longfei Li,Jun Zhou,Xiang Wang,Xiangnan He	2024	0
10	Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning Yongduo Sui,Wenyu Mao,Shuyao Wang,Xiang Wang,Jiancan Wu,Xiangnan He,Tat‐Seng Chua	2024	14
11	EXGC: Bridging Efficiency and Explainability in Graph Condensation Junfeng Fang,Xinglin Li,Yongduo Sui,Yuan Gao,Guibin Zhang,Kun Wang,Xiang Wang,Xiangnan He	2024	12
12	Large Language Models for Recommendation: Progresses and Future Directions Jizhi Zhang,Keqin Bao,Yang Zhang,Wenjie Wang,Fuli Feng,Xiangnan He	2024	6
13	On the Theories Behind Hard Negative Sampling for Recommendation Wentao Shi,Jiawei Chen,Fuli Feng,Jizhi Zhang,Junkang Wu,Chongming Gao,Xiangnan He	2023	15
14	Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation Jizhi Zhang,Keqin Bao,Yang Zhang,Wenjie Wang,Fuli Feng,Xiangnan He	2023	70
15	TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationbreakdown → Keqin Bao,Jizhi Zhang,Yang Zhang,Wenjie Wang,Fuli Feng,Xiangnan He	2023	155
16	Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation Chongming Gao,Kexin Huang,Jiawei Chen,Yuan Zhang,Biao Li,Peng Jiang,Shiqi Wang,Zhong Zhang,Xiangnan He	2023	28
17	Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum Yuan Gao,Xiang Wang,Xiangnan He,Zhenguang Liu,Huamin Feng,Yongdong Zhang	2023	60
18	ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction Haoxuan Li,Taojun Hu,Zetong Xiong,Chunyuan Zheng,Fuli Feng,Xiangnan He,Xiao‐Hua Zhou	2023	1
19	Adversarial Training Towards Robust Multimedia Recommender System Jinhui Tang,Xiaoyu Du,Xiangnan He,Fajie Yuan,Qi Tian,Tat‐Seng Chua	2019	131
20	Mining Scientific Terms and their Definitions: A Study of the ACL Anthology Yiping Jin,Min‐Yen Kan,Jun-Ping Ng,Xiangnan He	2013	22

About Xiangnan He

Xiangnan He is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 275 papers that have together received 17.3k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (148 papers), Advanced Graph Neural Networks (97 papers), Topic Modeling (76 papers), Advanced Bandit Algorithms Research (35 papers), Image Retrieval and Classification Techniques (25 papers), Multimodal Machine Learning Applications (24 papers), Advanced Image and Video Retrieval Techniques (20 papers) and Complex Network Analysis Techniques (18 papers). The work is most often cited by research in Information Systems (10.8k citations), Artificial Intelligence (11.5k citations) and Computer Vision and Pattern Recognition (5.0k citations). Xiangnan He has collaborated with scholars based in China, Singapore and United States. Frequent 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.

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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