Chuhan Wu

6.6k total citations · 4 hit papers
107 papers, 3.3k citations indexed

About

Chuhan Wu is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chuhan Wu has authored 107 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 89 papers in Artificial Intelligence, 50 papers in Information Systems and 19 papers in Computer Vision and Pattern Recognition. Recurrent topics in Chuhan Wu's work include Topic Modeling (62 papers), Recommender Systems and Techniques (41 papers) and Advanced Text Analysis Techniques (20 papers). Chuhan Wu is often cited by papers focused on Topic Modeling (62 papers), Recommender Systems and Techniques (41 papers) and Advanced Text Analysis Techniques (20 papers). Chuhan Wu collaborates with scholars based in China, Japan and United States. Chuhan Wu's co-authors include Fangzhao Wu, Yongfeng Huang, Xing Xie, Tao Qi, Lingjuan Lyu, Mingxiao An, Sixing Wu, Junxin Liu, Zhigang Yuan and Wei Lin and has published in prestigious journals such as Angewandte Chemie International Edition, Nature Communications and Expert Systems with Applications.

In The Last Decade

Chuhan Wu

97 papers receiving 3.1k citations

Hit Papers

Communication-efficient federated learning via knowledge ... 2020 2026 2022 2024 2022 2020 2022 2024 100 200 300
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. Communication-efficient federated learning via knowledge distillation
    2022 314 citations Chuhan Wu, Fangzhao Wu et al. profile →
  2. MIND: A Large-scale Dataset for News Recommendation
    2020 238 citations Fangzhao Wu, Chuhan Wu et al. profile →
  3. A federated graph neural network framework for privacy-preserving personalization
    2022 139 citations Chuhan Wu, Fangzhao Wu et al. profile →
  4. How Can Recommender Systems Benefit from Large Language Models: A Survey
    2024 53 citations Jianghao Lin, Xinyi Dai et al. ACM Transactions on Information Systems profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chuhan Wu China 31 2.5k 1.7k 468 224 214 107 3.3k
Shuming Shi China 31 2.3k 0.9× 547 0.3× 644 1.4× 243 1.1× 107 0.5× 184 3.4k
Chuan Zhou China 33 2.1k 0.8× 891 0.5× 451 1.0× 559 2.5× 244 1.1× 154 3.3k
Zheng Li China 25 779 0.3× 885 0.5× 249 0.5× 770 3.4× 128 0.6× 209 2.4k
Ruixuan Li China 26 1.0k 0.4× 800 0.5× 289 0.6× 748 3.3× 221 1.0× 262 2.6k
Yi Tay Singapore 26 2.1k 0.8× 954 0.6× 680 1.5× 169 0.8× 120 0.6× 71 3.0k
Amin Beheshti Australia 24 879 0.3× 460 0.3× 255 0.5× 450 2.0× 133 0.6× 136 2.0k
Jinlan Fu China 15 2.1k 0.8× 439 0.3× 538 1.1× 124 0.6× 81 0.4× 28 2.9k
Ziyu Guan China 28 1.6k 0.6× 923 0.5× 1.1k 2.2× 242 1.1× 109 0.5× 133 2.9k
Dae‐Won Kim South Korea 24 1.7k 0.7× 430 0.3× 1.1k 2.4× 78 0.3× 59 0.3× 125 2.6k

Countries citing papers authored by Chuhan Wu

Since Specialization
Citations

This map shows the geographic impact of Chuhan Wu'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 Chuhan Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chuhan Wu more than expected).

Fields of papers citing papers by Chuhan Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chuhan Wu. 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 Chuhan Wu. The network helps show where Chuhan Wu may publish in the future.

Co-authorship network of co-authors of Chuhan Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Chuhan Wu. A scholar is included among the top collaborators of Chuhan Wu 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 Chuhan Wu. Chuhan Wu 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.
2.
Wu, Chuhan, et al.. (2025). Learning Human Feedback from Large Language Models for Content Quality-aware Recommendation. ACM Transactions on Information Systems. 43(4). 1–28. 1 indexed citations
3.
Qi, T. Y., et al.. (2025). ModelShield: Adaptive and Robust Watermark Against Model Extraction Attack. IEEE Transactions on Information Forensics and Security. 20. 1767–1782. 2 indexed citations
4.
Du, Zhaocheng, et al.. (2025). Few-shot LLM Synthetic Data with Distribution Matching. 432–441.
5.
Li, Yusheng, et al.. (2025). A novel approach for geometric accuracy synthesis of three degrees of freedom parallel mechanism. Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science. 239(9). 3166–3178. 1 indexed citations
6.
Jiang, Yuxin, Chuhan Wu, Wanjun Zhong, et al.. (2024). Learning to Edit: Aligning LLMs with Knowledge Editing. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 4689–4705. 4 indexed citations
7.
Du, Zhaocheng, et al.. (2024). A Tutorial on Feature Interpretation in Recommender Systems. 1281–1282. 2 indexed citations
8.
Du, Zhaocheng, et al.. (2024). LightCS: Selecting Quadratic Feature Crosses in Linear Complexity. 38–46. 1 indexed citations
9.
Lin, Jianghao, Xinyi Dai, Weiwen Liu, et al.. (2024). How Can Recommender Systems Benefit from Large Language Models: A Survey. ACM Transactions on Information Systems. 43(2). 1–47. 53 indexed citations breakdown →
10.
11.
Chen, Yaoran, Leilei Cao, Dan Zhang, et al.. (2023). WindFix: Harnessing the power of self-supervised learning for versatile imputation of offshore wind speed time series. Energy. 287. 128995–128995. 10 indexed citations
12.
Han, Sungwon, Fangzhao Wu, Sundong Kim, et al.. (2023). DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision. 3766–3774. 3 indexed citations
13.
Wu, Fangzhao, et al.. (2021). Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2814–2824. 34 indexed citations
14.
Wu, Chuhan, Fangzhao Wu, Tao Qi, & Yongfeng Huang. (2021). Fastformer: Additive Attention is All You Need. arXiv (Cornell University). 1 indexed citations
15.
Qi, Tao, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, & Xing Xie. (2020). Privacy-Preserving News Recommendation Model Learning. 1423–1432. 85 indexed citations
16.
Wu, Chuhan, Fangzhao Wu, Tao Qi, & Yongfeng Huang. (2020). SentiRec: Sentiment Diversity-aware Neural News Recommendation. 44–53. 21 indexed citations
17.
Qi, Tao, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, & Xing Xie. (2020). Privacy-Preserving News Recommendation Model Training via Federated Learning.. arXiv (Cornell University). 6 indexed citations
18.
19.
Wu, Chuhan, Fangzhao Wu, Suyu Ge, et al.. (2019). Neural News Recommendation with Multi-Head Self-Attention. 6388–6393. 188 indexed citations
20.
Wu, Chuhan, Fangzhao Wu, Yongfeng Huang, Sixing Wu, & Zhigang Yuan. (2017). THU_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM. International Joint Conference on Natural Language Processing. 47–52. 15 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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