Yingqiang Ge

2.2k total citations · 1 hit paper
27 papers, 722 citations indexed

About

Yingqiang Ge is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Yingqiang Ge has authored 27 papers receiving a total of 722 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 15 papers in Information Systems and 7 papers in Management Science and Operations Research. Recurrent topics in Yingqiang Ge's work include Recommender Systems and Techniques (14 papers), Explainable Artificial Intelligence (XAI) (8 papers) and Topic Modeling (8 papers). Yingqiang Ge is often cited by papers focused on Recommender Systems and Techniques (14 papers), Explainable Artificial Intelligence (XAI) (8 papers) and Topic Modeling (8 papers). Yingqiang Ge collaborates with scholars based in United States, China and Netherlands. Yingqiang Ge's co-authors include Yongfeng Zhang, Zuohui Fu, Shijie Geng, Shuchang Liu, Yunqi Li, Shuyuan Xu, Juntao Tan, Hanxiong Chen, Gerard de Melo and Zelong Li and has published in prestigious journals such as Journal of Molecular Liquids, Journal of the Association for Information Science and Technology and ACM Transactions on Intelligent Systems and Technology.

In The Last Decade

Yingqiang Ge

25 papers receiving 710 citations

Hit Papers

Recommendation as Language Processing (RLP): A Unified Pr... 2022 2026 2023 2024 2022 50 100 150 200
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. Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
    2022 223 citations Shijie Geng, Shuchang Liu et al. profile →

Peers

Yingqiang Ge
Zuohui Fu United States
Florian Tramèr United States
Claudio Orlandi Denmark
Adith Swaminathan United States
Jinghua Piao China
Ana Peleteiro Spain
Matevž Kunaver Slovenia
Sudip Roy United States
Long Xia China
Sahin Cem Geyik United States
Zuohui Fu United States
Yingqiang Ge
Citations per year, relative to Yingqiang Ge Yingqiang Ge (= 1×) peers Zuohui Fu

Countries citing papers authored by Yingqiang Ge

Since Specialization
Citations

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

Fields of papers citing papers by Yingqiang Ge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yingqiang Ge

This figure shows the co-authorship network connecting the top 25 collaborators of Yingqiang Ge. A scholar is included among the top collaborators of Yingqiang Ge 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 Yingqiang Ge. Yingqiang Ge 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.
Wu, Yunfan, Yingqiang Ge, Shuchang Liu, et al.. (2025). The 1st Workshop on Human-Centered Recommender Systems. 2088–2091. 1 indexed citations
2.
Ge, Yingqiang, et al.. (2025). Examining the role of social media in fostering responsible consumption: Insights from Bitcoin Adoption trends (2015–2023). Cleaner and Responsible Consumption. 17. 100266–100266. 1 indexed citations
3.
Xu, Shuyuan, et al.. (2025). Causal Inference for Recommendation: Foundations, Methods, and Applications. ACM Transactions on Intelligent Systems and Technology. 16(3). 1–51. 1 indexed citations
4.
Tan, Juntao, Shuyuan Xu, Wenyue Hua, et al.. (2024). IDGenRec: LLM-RecSys Alignment with Textual ID Learning. 355–364. 12 indexed citations
5.
Ge, Yingqiang, Shuchang Liu, Zuohui Fu, et al.. (2024). A Survey on Trustworthy Recommender Systems. 3(2). 1–68. 17 indexed citations
6.
Hua, Wenyue, et al.. (2024). UP5: Unbiased Foundation Model for Fairness-aware Recommendation. 1899–1912.
7.
Li, Yunqi, Hanxiong Chen, Shuyuan Xu, et al.. (2023). Fairness in Recommendation: Foundations, Methods, and Applications. ACM Transactions on Intelligent Systems and Technology. 14(5). 1–48. 35 indexed citations
8.
Wang, Hongpeng, Jiayao Yang, Kexin Huang, et al.. (2023). Effects of deep eutectic solvents on the biotransformation efficiency of ω-transaminase. Journal of Molecular Liquids. 377. 121379–121379. 15 indexed citations
9.
Geng, Shijie, Zuohui Fu, Yingqiang Ge, et al.. (2022). Improving Personalized Explanation Generation through Visualization. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 244–255. 20 indexed citations
10.
Gao, Ruoyuan, Yingqiang Ge, & Chirag Shah. (2022). FAIR: Fairness‐aware information retrieval evaluation. Journal of the Association for Information Science and Technology. 73(10). 1461–1473. 6 indexed citations
11.
Geng, Shijie, Zuohui Fu, Juntao Tan, et al.. (2022). Path Language Modeling over Knowledge Graphsfor Explainable Recommendation. Proceedings of the ACM Web Conference 2022. 946–955. 46 indexed citations
12.
Tan, Juntao, Shijie Geng, Zuohui Fu, et al.. (2022). Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning. Proceedings of the ACM Web Conference 2022. 1018–1027. 49 indexed citations
13.
Ge, Yingqiang, et al.. (2022). Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning. arXiv (Cornell University). 316–324. 40 indexed citations
14.
Li, Zelong, et al.. (2022). AutoLossGen: Automatic Loss Function Generation for Recommender Systems. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1304–1315. 9 indexed citations
15.
Ge, Yingqiang. (2021). The influence of news communication for production mode on computer news dissemination. The Frontiers of Society Science and Technology. 3(4). 1 indexed citations
16.
Xu, Shuyuan, Yunqi Li, Shuchang Liu, et al.. (2021). Learning causal explanations for recommendation. 2911. 13–25. 5 indexed citations
17.
Li, Yunqi, Yingqiang Ge, & Yongfeng Zhang. (2021). Tutorial on Fairness of Machine Learning in Recommender Systems. 2654–2657. 32 indexed citations
18.
Li, Yunqi, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, & Yongfeng Zhang. (2021). Towards Personalized Fairness based on Causal Notion. 1054–1063. 85 indexed citations
19.
Li, Yunqi, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, & Yongfeng Zhang. (2021). Personalized Counterfactual Fairness in Recommendation. arXiv (Cornell University).
20.
Wang, Guang, Wenzhong Li, Jun Zhang, et al.. (2019). sharedCharging. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 3(3). 1–25. 38 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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