Bee-Chung Chen
Impact in
- Information Systems top 0.5%
- Recommender Systems and Techniques
-
- Advanced Bandit Algorithms Research
Papers in
-
- Recommender Systems and Techniques 22
- Data Mining Algorithms and Applications 6
- Expert finding and Q&A systems 5
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- Topic Modeling 6
- Data Stream Mining Techniques 6
- Co-authors
- Deepak Agarwal (21 shared papers)Pradheep Elango (7 shared papers)Kristen LeFevre (4 shared papers)Raghu Ramakrishnan (6 shared papers)Daniel Kifer (2 shared papers)Ashwin Machanavajjhala (2 shared papers)Bo Long (2 shared papers)Deepak Agarwal (5 shared papers)
- Journals
- Communications of the ACM (1 paper)The VLDB Journal (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)Data Mining and Knowledge Discovery (1 paper)Minds at UW (University of Wisconsin) (1 paper)
- Partner nations
- United StatesUnited KingdomTaiwan
In The Last Decade
Bee-Chung Chen
45 papers receiving 2.0k citations
Bee-Chung Chen's Hit Papers
Peers
Comparison fields: 5 of 106
- Information Systems 1.2k
- Management Science and Operations Research 507
- Artificial Intelligence 1.2k
- Computer Science Applications 131
- Computational Mathematics 12
Countries citing papers authored by Bee-Chung Chen
This map shows the geographic impact of Bee-Chung Chen'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 Bee-Chung Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bee-Chung Chen more than expected).
Fields of papers citing papers by Bee-Chung Chen
This network shows the impact of papers produced by Bee-Chung Chen. 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 Bee-Chung Chen. The network helps show where Bee-Chung Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Bee-Chung Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Regression-based latent factor models Hit paper breakdown → | 2009 | 385 |
| 2 | 2010 | 206 | |
| 3 | 2009 | 120 | |
| 4 | Privacy skyline: privacy with multidimensional adversarial knowledge | 2007 | 112 |
| 5 | 2009 | 111 | |
| 6 | 2009 | 94 | |
| 7 | 2009 | 84 | |
| 8 | Example-driven design of efficient record matching queries | 2007 | 77 |
| 9 | Online Models for Content Optimization | 2008 | 74 |
| 10 | 2011 | 59 | |
| 11 | 2011 | 57 | |
| 12 | 2016 | 54 | |
| 13 | Prediction cubes | 2005 | 48 |
| 14 | 2010 | 47 | |
| 15 | 2017 | 47 | |
| 16 | 2016 | 43 | |
| 17 | 2011 | 40 | |
| 18 | 1972 | 36 | |
| 19 | 2011 | 36 | |
| 20 | 2015 | 36 |
About Bee-Chung Chen
Bee-Chung Chen is a scholar working on Information Systems, Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications and Signal Processing, having authored 47 papers that have together received 2.1k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (22 papers), Advanced Bandit Algorithms Research (13 papers), Advanced Database Systems and Queries (6 papers), Data Mining Algorithms and Applications (6 papers), Data Management and Algorithms (6 papers), Topic Modeling (6 papers), Data Stream Mining Techniques (6 papers) and Expert finding and Q&A systems (5 papers). The work is most often cited by research in Information Systems (1.2k citations), Management Science and Operations Research (507 citations), Artificial Intelligence (1.2k citations), Computer Science Applications (131 citations) and Computational Mathematics (12 citations). Bee-Chung Chen has collaborated with scholars based in United States, United Kingdom and Taiwan. Frequent co-authors include Deepak Agarwal, Pradheep Elango, Kristen LeFevre, Raghu Ramakrishnan, Daniel Kifer, Ashwin Machanavajjhala, Bo Long, Deepak Agarwal, Raghu Ramakrishnan and Xuanhui Wang. Their work appears in journals such as Communications of the ACM, The VLDB Journal, ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery and Minds at UW (University of Wisconsin).
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.