Min Zhang

7.1k total citations · 3 hit papers
181 papers, 4.0k citations indexed

About

Min Zhang is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Min Zhang has authored 181 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 129 papers in Information Systems, 88 papers in Artificial Intelligence and 31 papers in Computer Vision and Pattern Recognition. Recurrent topics in Min Zhang's work include Recommender Systems and Techniques (72 papers), Information Retrieval and Search Behavior (54 papers) and Expert finding and Q&A systems (30 papers). Min Zhang is often cited by papers focused on Recommender Systems and Techniques (72 papers), Information Retrieval and Search Behavior (54 papers) and Expert finding and Q&A systems (30 papers). Min Zhang collaborates with scholars based in China, United States and United Kingdom. Min Zhang's co-authors include Shaoping Ma, Yiqun Liu, Chong Chen, Weizhi Ma, William D. Heavlin, Andrew Tomkins, Chenyang Wang, Jiaxin Mao, Yongfeng Zhang and Liyun Ru and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Chemical Engineering Journal.

In The Last Decade

Min Zhang

162 papers receiving 3.9k citations

Hit Papers

Neural Attentional Rating Regression with Review-level Ex... 2017 2026 2020 2023 2018 2017 2022 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. Neural Attentional Rating Regression with Review-level Explanations
    2018 374 citations Min Zhang, Yiqun Liu et al. profile →
  2. Reviewer bias in single- versus double-blind peer review
    2017 296 citations Min Zhang et al. profile →
  3. A Survey on the Fairness of Recommender Systems
    2022 133 citations Weizhi Ma, Min Zhang 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
Min Zhang China 34 2.8k 2.3k 634 443 339 181 4.0k
Yiqun Liu China 39 3.7k 1.3× 3.3k 1.4× 960 1.5× 598 1.3× 400 1.2× 301 5.6k
Filip Radlinski United States 30 2.6k 0.9× 2.2k 0.9× 727 1.1× 732 1.7× 217 0.6× 66 4.1k
Joemon M. Jose United Kingdom 32 2.5k 0.9× 1.9k 0.8× 1.2k 1.8× 423 1.0× 399 1.2× 235 4.3k
Shaoping Ma China 40 4.2k 1.5× 3.8k 1.6× 1.2k 1.8× 664 1.5× 418 1.2× 278 6.2k
Iván Cantador Spain 30 2.0k 0.7× 1.3k 0.6× 568 0.9× 439 1.0× 273 0.8× 94 2.8k
Paul N. Bennett United States 30 1.6k 0.6× 2.2k 0.9× 690 1.1× 298 0.7× 224 0.7× 122 3.9k
Sean M. McNee United States 13 2.5k 0.9× 1.2k 0.5× 622 1.0× 710 1.6× 312 0.9× 15 3.0k
F. Maxwell Harper United States 23 2.9k 1.0× 2.1k 0.9× 882 1.4× 711 1.6× 721 2.1× 38 4.7k
Min‐Yen Kan Singapore 38 2.5k 0.9× 4.6k 2.0× 900 1.4× 495 1.1× 240 0.7× 226 6.0k
Shyong K. Lam United States 15 1.8k 0.7× 1.0k 0.4× 457 0.7× 403 0.9× 395 1.2× 22 2.6k

Countries citing papers authored by Min Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Min Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Min Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Min Zhang. A scholar is included among the top collaborators of Min Zhang 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 Min Zhang. Min Zhang 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.
He, Jianfeng, Changbin Li, Min Zhang, et al.. (2025). Survey of uncertainty estimation in LLMs - Sources, methods, applications, and challenges. Information Fusion. 130. 104057–104057.
2.
Ma, Xiaohan, Min Zhang, Yuxin Li, et al.. (2025). Pyroligneous acid amendments alleviated antibiotic resistance genes pollution in agricultural soil via inhibiting horizontal gene transformation. Chemical Engineering Journal. 506. 160070–160070. 2 indexed citations
3.
4.
Ai, Qingyao, Yixiao Ma, Haitao Li, et al.. (2025). Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions. ACM Transactions on Information Systems. 43(5). 1–27.
5.
Ma, Weizhi, et al.. (2025). User Immersion-aware Short Video Recommendation. ACM Transactions on Information Systems. 44(1). 1–33.
6.
Zhang, Xiufeng, Xuehang Song, Xingyu Li, et al.. (2025). Supercritical CO2 Injection-Induced Fracturing in Longmaxi Shales: A Laboratory Study. Energies. 18(4). 855–855.
7.
Zhang, Min, et al.. (2024). Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization. Heliyon. 10(20). e38865–e38865. 2 indexed citations
8.
Gao, Chongming, et al.. (2024). EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems. 977–987. 4 indexed citations
9.
10.
11.
Zhang, Hongbin, Kehai Chen, Xuefeng Bai, Yang Xiang, & Min Zhang. (2024). Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language Model. 13816–13836. 1 indexed citations
12.
Sun, Peijie, et al.. (2023). Understanding User Immersion in Online Short Video Interaction. 731–740. 6 indexed citations
13.
Xie, Xiaohui, et al.. (2023). Relevance Feedback with Brain Signals. ACM Transactions on Information Systems. 42(4). 1–37. 9 indexed citations
14.
Ma, Weizhi, et al.. (2023). Measuring Item Global Residual Value for Fair Recommendation. 269–278. 2 indexed citations
15.
Zhang, Min, et al.. (2017). Hybrid recommendation approach enhanced by deep learning. Journal of Tsinghua University(Science and Technology). 57(10). 1014–1021. 4 indexed citations
16.
Ma, Weizhi, Min Zhang, Yiqun Liu, & Shaoping Ma. (2016). Multi-grained role labeling based on multi-modality information for real customer service telephone conversation. International Joint Conference on Artificial Intelligence. 1816–1822. 2 indexed citations
17.
Zhang, Min, et al.. (2016). Rating-boosted latent topics: understanding users and items with ratings and reviews. International Joint Conference on Artificial Intelligence. 2640–2646. 95 indexed citations
18.
Zhang, Min, et al.. (2008). Discussion Search in Enterprise Email Archives. Zhongwen xinxi xuebao. 22(2). 81–86. 1 indexed citations
19.
Liu, Yiqun, Canhui Wang, Min Zhang, & Shaoping Ma. (2004). Finding "Abstract Fields" of Web Pages and Query Specific Retrieval -- THUIR at TREC 2004 web track *. Text REtrieval Conference. 1 indexed citations
20.
Zhang, Min. (2003). Using data dictionary to implement the schema evolution of object-oriented database. Applied science and technology.

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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