Fajie Yuan
- Information Systems top 0.5%
- Recommender Systems and Techniques 23
- Artificial Intelligence top 2%
- Topic Modeling 19
- Advanced Graph Neural Networks 6
- Domain Adaptation and Few-Shot Learning 5
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- Advanced Bandit Algorithms Research 9
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- Multimodal Machine Learning Applications 7
- Transportation top 5%
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- Genomics and Phylogenetic Studies 4
- Machine Learning in Bioinformatics 3
- Co-authors
- Xiangnan HeJoemon M. JoseAlexandros KaratzoglouIoannis ArapakisGuibing GuoLong ChenHai-Tao YuTat‐Seng Chua
- Journals
- IEEE Transactions on Knowledge and Data Engineering (3 papers)Information Sciences (1 paper)Journal of the American Chemical Society (1 paper)
- Partner nations
- ChinaUnited KingdomJapan
In The Last Decade
Fajie Yuan
41 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 70
- Information Systems 925
- Artificial Intelligence 815
- Management Science and Operations Research 224
- Computer Vision and Pattern Recognition 318
- Transportation 86
Countries citing papers authored by Fajie Yuan
This map shows the geographic impact of Fajie Yuan'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 Fajie Yuan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fajie Yuan more than expected).
Fields of papers citing papers by Fajie Yuan
This network shows the impact of papers produced by Fajie Yuan. 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 Fajie Yuan. The network helps show where Fajie Yuan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Fajie Yuan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 8 | |
| 5 | 2025 | 2 | |
| 6 | 2025 | 1 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 2 | |
| 9 | 2024 | 8 | |
| 10 | 2024 | 12 | |
| 11 | 2024 | 2 | |
| 12 | 2024 | 37 | |
| 13 | 2022 | 7 | |
| 14 | 2021 | 18 | |
| 15 | 2019 | 131 | |
| 16 | Modeling the Past and Future Contexts for Session-based Recommendation. | 2019 | 1 |
| 17 | A Simple but Hard-to-Beat Baseline for Session-based Recommendations. | 2018 | 4 |
| 18 | f BGD : Learning Embeddings From Positive Unlabeled Data with BGD. | 2018 | 14 |
| 19 | 2018 | 18 | |
| 20 | 2017 | 10 |
About Fajie Yuan
Fajie Yuan is a scholar working on Information Systems, Artificial Intelligence and Management Science and Operations Research, having authored 45 papers that have together received 1.2k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (23 papers), Topic Modeling (19 papers), Advanced Bandit Algorithms Research (9 papers), Multimodal Machine Learning Applications (7 papers), Advanced Graph Neural Networks (6 papers), Domain Adaptation and Few-Shot Learning (5 papers), Genomics and Phylogenetic Studies (4 papers) and Machine Learning in Bioinformatics (3 papers). The work is most often cited by research in Information Systems (925 citations), Artificial Intelligence (815 citations) and Management Science and Operations Research (224 citations). Fajie Yuan has collaborated with scholars based in China, United Kingdom and Japan. Frequent co-authors include Xiangnan He, Joemon M. Jose, Alexandros Karatzoglou, Ioannis Arapakis, Guibing Guo, Long Chen, Hai-Tao Yu, Tat‐Seng Chua, Jinhui Tang and Xiaoyu Du. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Information Sciences, Journal of the American Chemical Society, ACM Transactions on Information Systems and Nature Communications.
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.