Beidou Wang
Impact in
- Information Systems top 2%
- Recommender Systems and Techniques
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
Papers in
-
- Recommender Systems and Techniques 10
- Expert finding and Q&A systems 3
-
- Topic Modeling 3
- Sentiment Analysis and Opinion Mining 2
- Text and Document Classification Technologies 2
- Co-authors
- Ziyu Guan (9 shared papers)Deng Cai (5 shared papers)Yu Zhu (7 shared papers)Haifeng Liu (2 shared papers)Hao Li (1 shared paper)Xiaofei He (2 shared papers)Jiajun Bu (6 shared papers)Wei Zhao (2 shared papers)
- Journals
- IEEE Transactions on Knowledge and Data Engineering (2 papers)Neurocomputing (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- ChinaCanadaUnited States
In The Last Decade
Beidou Wang
12 papers receiving 693 citations
Beidou Wang's Hit Papers
Peers
Comparison fields: 5 of 79
- Information Systems 436
- Artificial Intelligence 433
- Transportation 89
- Management Science and Operations Research 118
- Computer Vision and Pattern Recognition 138
Countries citing papers authored by Beidou Wang
This map shows the geographic impact of Beidou Wang'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 Beidou Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beidou Wang more than expected).
Fields of papers citing papers by Beidou Wang
This network shows the impact of papers produced by Beidou Wang. 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 Beidou Wang. The network helps show where Beidou Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Beidou Wang, 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 | What to Do Next: Modeling User Behaviors by Time-LSTM Hit paper breakdown → | 2017 | 311 |
| 2 | 2017 | 128 | |
| 3 | 2019 | 108 | |
| 4 | 2013 | 58 | |
| 5 | 2016 | 21 | |
| 6 | 2014 | 20 | |
| 7 | 2016 | 20 | |
| 8 | 2018 | 15 | |
| 9 | 2019 | 14 | |
| 10 | 2013 | 13 | |
| 11 | 2018 | 3 | |
| 12 | 2019 | 1 |
About Beidou Wang
Beidou Wang is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 12 papers that have together received 712 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (10 papers), Expert finding and Q&A systems (3 papers), Advanced Bandit Algorithms Research (3 papers), Topic Modeling (3 papers), Caching and Content Delivery (2 papers), Optimization and Search Problems (2 papers), Sentiment Analysis and Opinion Mining (2 papers) and Text and Document Classification Technologies (2 papers). The work is most often cited by research in Information Systems (436 citations), Artificial Intelligence (433 citations), Transportation (89 citations), Management Science and Operations Research (118 citations) and Computer Vision and Pattern Recognition (138 citations). Beidou Wang has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Ziyu Guan, Deng Cai, Yu Zhu, Haifeng Liu, Hao Li, Xiaofei He, Jiajun Bu, Deng Cai, Wei Zhao and Quan Wang. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Neurocomputing 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.