Le Wu

6.2k total citations · 4 hit papers
117 papers, 3.7k citations indexed

About

Le Wu is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Le Wu has authored 117 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Artificial Intelligence, 62 papers in Information Systems and 25 papers in Computer Vision and Pattern Recognition. Recurrent topics in Le Wu's work include Recommender Systems and Techniques (56 papers), Advanced Graph Neural Networks (41 papers) and Topic Modeling (24 papers). Le Wu is often cited by papers focused on Recommender Systems and Techniques (56 papers), Advanced Graph Neural Networks (41 papers) and Topic Modeling (24 papers). Le Wu collaborates with scholars based in China, United States and Poland. Le Wu's co-authors include Meng Wang, Richang Hong, Peijie Sun, Kun Zhang, Yanjie Fu, Enhong Chen, Xiting Wang, Lei Chen, Xiangnan He and Qi Liu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Information Sciences.

In The Last Decade

Le Wu

110 papers receiving 3.6k citations

Hit Papers

align trajectories

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.

Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

2020 374 citations Le Wu, Richang Hong et al. profile →
A Neural Influence Diffusion Model for Social Recommendation

2019 365 citations Le Wu, Peijie Sun et al. profile →
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

2022 250 citations Le Wu, Meng Wang et al. profile →
DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

2020 222 citations Le Wu, Junwei Li et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Le Wu China	31	2.4k	2.3k	757	371	305	117	3.7k
Fuzheng Zhang China	22	3.6k 1.5×	3.0k 1.3×	852 1.1×	354 1.0×	568 1.9×	52	4.7k
Yongfeng Zhang United States	32	3.2k 1.3×	2.8k 1.2×	844 1.1×	206 0.6×	561 1.8×	107	4.2k
Chen Gao China	25	1.5k 0.6×	1.6k 0.7×	463 0.6×	215 0.6×	359 1.2×	95	2.5k
Fangzhao Wu China	37	3.5k 1.4×	2.1k 0.9×	644 0.9×	306 0.8×	192 0.6×	118	4.2k
Lizhen Cui China	35	2.2k 0.9×	2.1k 0.9×	606 0.8×	1.0k 2.8×	266 0.9×	324	4.6k
Chenliang Li China	32	3.4k 1.4×	1.5k 0.7×	595 0.8×	232 0.6×	418 1.4×	134	4.5k
Zhoujun Li China	40	3.5k 1.4×	1.3k 0.6×	1.1k 1.5×	644 1.7×	205 0.7×	338	5.2k
Ziyu Guan China	28	1.6k 0.7×	923 0.4×	1.1k 1.4×	242 0.7×	194 0.6×	133	2.9k
Arjen P. de Vries Netherlands	30	1.6k 0.7×	2.0k 0.9×	850 1.1×	508 1.4×	409 1.3×	229	3.7k

Countries citing papers authored by Le Wu

Since Specialization

Citations

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

Fields of papers citing papers by Le Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Le Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Le Wu. A scholar is included among the top collaborators of Le 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 Le Wu. Le Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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

20 of 20 papers shown

1.

Chu, Samuel Kai Wah, et al.. (2025). MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning. 1311–1324.

2.

Wu, Le, Jindong Wang, Ling Chen, et al.. (2025). Trustworthy Knowledge Discovery and Data Mining (TrustKDD). 6930–6932.

3.

Wu, Le, et al.. (2025). Fair Personalized Learner Modeling Without Sensitive Attributes. 4612–4624. 1 indexed citations

4.

Wu, Le, et al.. (2025). InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models. 472–483.

5.

Li, Junwei, et al.. (2024). Dual Graph Neural Networks for Dynamic Users’ Behavior Prediction on Social Networking Services. IEEE Transactions on Computational Social Systems. 11(5). 7020–7031. 4 indexed citations

6.

Zhu, Yanan, Jiaqiu Ai, Le Wu, et al.. (2024). An Active Multi-Target Domain Adaptation Strategy: Progressive Class Prototype Rectification. IEEE Transactions on Multimedia. 27. 1874–1886. 18 indexed citations

7.

Wu, Le, Qi Liu, Zhenya Huang, et al.. (2024). Understanding and improving fairness in cognitive diagnosis. Science China Information Sciences. 67(5). 10 indexed citations

8.

Wu, Le, et al.. (2024). Multimodality Invariant Learning for Multimedia-Based New Item Recommendation. arXiv (Cornell University). 677–686. 9 indexed citations

9.

Shen, Shuanghong, Linbo Zhu, Le Wu, et al.. (2023). Global and local neural cognitive modeling for student performance prediction. Expert Systems with Applications. 237. 121637–121637. 5 indexed citations

10.

Tu, Ke, et al.. (2023). Disentangled Interest importance aware Knowledge Graph Neural Network for Fund Recommendation. 2482–2491. 4 indexed citations

11.

Lian, Defu, et al.. (2023). RecStudio: Towards a Highly-Modularized Recommender System. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2890–2900. 1 indexed citations

12.

Xue, Feng, et al.. (2022). MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation. ACM Transactions on Information Systems. 41(2). 1–27. 28 indexed citations

13.

Zhuang, Ting, Beibei Wang, Le Wu, et al.. (2022). TRIM3 facilitates estrogen signaling and modulates breast cancer cell progression. Cell Communication and Signaling. 20(1). 45–45. 9 indexed citations

14.

Bu, Chenyang, et al.. (2022). Which Companies are Likely to Invest: Knowledge-graph-based Recommendation for Investment Promotion. 10. 11–20. 1 indexed citations

15.

Wu, Le, Peijie Sun, Richang Hong, Yong Ge, & Meng Wang. (2018). Collaborative Neural Social Recommendation. IEEE Transactions on Systems Man and Cybernetics Systems. 51(1). 464–476. 81 indexed citations

16.

Wu, Le, Yong Ge, Qi Liu, et al.. (2018). Explainable Social Contextual Image Recommendation with Hierarchical Attention.. arXiv (Cornell University). 1 indexed citations

17.

Jin, Xin, et al.. (2016). Efficient Deep Aesthetic Image Classification using Connected Local and Global Features. arXiv (Cornell University). 2 indexed citations

18.

Wu, Le. (2014). A Learning Algorithm for Parameters of Automatic Disturbances Rejection Controller. Acta Automatica Sinica. 9 indexed citations

19.

Wu, Le & Min-Ling Zhang. (2013). Multi-Label Classification with Unlabeled Data: An Inductive Approach. Asian Conference on Machine Learning. 197–212. 17 indexed citations

20.

Wu, Le, et al.. (2011). Current situation, trends and countermeasures about China's grain consumption.. Nongye xiandaihua yanjiu. 32(2). 129–133. 2 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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