Qing Da
Impact in
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- Advanced Bandit Algorithms Research
- Information Systems top 5%
- Recommender Systems and Techniques
Papers in
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- Reinforcement Learning in Robotics 6
- Data Stream Mining Techniques 5
- Machine Learning and Data Classification 3
- Topic Modeling 2
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- Recommender Systems and Techniques 4
- Co-authors
- Anxiang Zeng (9 shared papers)Yang Yu (4 shared papers)Shiyong Chen (2 shared papers)Zhi‐Hua Zhou (4 shared papers)Yu Yang (2 shared papers)Yinghui Xu (1 shared paper)Jing-Cheng Shi (2 shared papers)Yujing Hu (1 shared paper)
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)Lecture notes in computer science (2 papers)Adaptive Agents and Multi-Agents Systems (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (5 papers)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Qing Da
18 papers receiving 448 citations
Peers
Comparison fields: 5 of 60
- Management Science and Operations Research 164
- Information Systems 230
- Artificial Intelligence 285
- Computer Vision and Pattern Recognition 92
- Computer Science Applications 19
Countries citing papers authored by Qing Da
This map shows the geographic impact of Qing Da'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 Qing Da with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qing Da more than expected).
Fields of papers citing papers by Qing Da
This network shows the impact of papers produced by Qing Da. 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 Qing Da. The network helps show where Qing Da may publish in the future.
Co-authors
The 24 scholars most cited alongside Qing Da, 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 | 2018 | 100 | |
| 2 | 2019 | 97 | |
| 3 | 2018 | 94 | |
| 4 | 2014 | 67 | |
| 5 | 2020 | 56 | |
| 6 | 2018 | 14 | |
| 7 | 2021 | 13 | |
| 8 | 2022 | 7 | |
| 9 | 2019 | 4 | |
| 10 | 2022 | 4 | |
| 11 | 2020 | 2 | |
| 12 | 2014 | 2 | |
| 13 | Non-parameter Regression Model of Traffic Flow | 2003 | 1 |
| 14 | Expected utility model based on fuzzy prior probability | 2002 | 1 |
| 15 | 2022 | 1 | |
| 16 | 2021 | 1 | |
| 17 | 2019 | 1 | |
| 18 | 2022 | 1 |
About Qing Da
Qing Da is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Marketing and Computer Networks and Communications, having authored 18 papers that have together received 466 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Data Stream Mining Techniques (5 papers), Recommender Systems and Techniques (4 papers), Machine Learning and Data Classification (3 papers), Advanced Bandit Algorithms Research (3 papers), Consumer Market Behavior and Pricing (3 papers), Optimization and Search Problems (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Management Science and Operations Research (164 citations), Information Systems (230 citations), Artificial Intelligence (285 citations), Computer Vision and Pattern Recognition (92 citations) and Computer Science Applications (19 citations). Qing Da has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Anxiang Zeng, Yang Yu, Shiyong Chen, Zhi‐Hua Zhou, Yu Yang, Yinghui Xu, Jing-Cheng Shi, Yujing Hu, Jun Tan and Lijun Zhang. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering, Lecture notes in computer science, Adaptive Agents and Multi-Agents Systems and Proceedings of the AAAI Conference on Artificial Intelligence.
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.