Hande Dong
Impact in
- Information Systems top 2%
- Recommender Systems and Techniques
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- Advanced Bandit Algorithms Research
Papers in
-
- Recommender Systems and Techniques 2
- Software Engineering Research 1
- Web Data Mining and Analysis 1
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- Advanced Graph Neural Networks 1
- Machine Learning and Data Classification 1
- Co-authors
- Jiawei Chen (3 shared papers)Xiangnan He (2 shared papers)Fuli Feng (2 shared papers)Meng Wang (1 shared paper)Xiang Wang (1 shared paper)Peng Cui (1 shared paper)Yutao Xie (2 shared papers)Lei Zhang (1 shared paper)
- Journals
- ACM Transactions on Software Engineering and Methodology (1 paper)ACM Transactions on Information Systems (1 paper)
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Hande Dong
4 papers receiving 395 citations
Hande Dong's Hit Papers
Peers
Comparison fields: 5 of 46
- Information Systems 271
- Management Science and Operations Research 102
- Artificial Intelligence 251
- Computer Science Applications 24
- Computer Vision and Pattern Recognition 74
Countries citing papers authored by Hande Dong
This map shows the geographic impact of Hande Dong'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 Hande Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hande Dong more than expected).
Fields of papers citing papers by Hande Dong
This network shows the impact of papers produced by Hande Dong. 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 Hande Dong. The network helps show where Hande Dong may publish in the future.
Co-authors
The 11 scholars most cited alongside Hande Dong, 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 | Bias and Debias in Recommender System: A Survey and Future Directions Hit paper breakdown → | 2022 | 334 |
| 2 | 2021 | 58 | |
| 3 | 2023 | 11 | |
| 4 | 2024 | 1 |
About Hande Dong
Hande Dong is a scholar working on Information Systems, Artificial Intelligence, Statistical and Nonlinear Physics, Management Science and Operations Research and Computer Science Applications, having authored 4 papers that have together received 404 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (2 papers), Complex Network Analysis Techniques (1 paper), Software Engineering Research (1 paper), Advanced Graph Neural Networks (1 paper), Machine Learning and Data Classification (1 paper), Software Testing and Debugging Techniques (1 paper), Web Data Mining and Analysis (1 paper) and Advanced Bandit Algorithms Research (1 paper). The work is most often cited by research in Information Systems (271 citations), Management Science and Operations Research (102 citations), Artificial Intelligence (251 citations), Computer Science Applications (24 citations) and Computer Vision and Pattern Recognition (74 citations). Hande Dong has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Jiawei Chen, Xiangnan He, Fuli Feng, Meng Wang, Xiang Wang, Peng Cui, Yutao Xie, Lei Zhang, Zhonghai Wu and Yichong Leng. Their work appears in journals such as ACM Transactions on Software Engineering and Methodology and ACM Transactions on Information Systems.
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.