Dmitry Pavlov
Impact in
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- Mobile Crowdsensing and Crowdsourcing
- Information Systems top 5%
- Recommender Systems and Techniques
- Expert finding and Q&A systems
Papers in
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- Bayesian Modeling and Causal Inference 5
- Data Stream Mining Techniques 4
- Machine Learning and Data Classification 3
- Text and Document Classification Technologies 2
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- Data Mining Algorithms and Applications 4
- Co-authors
- David M. Pennock (2 shared papers)John Canny (3 shared papers)Ye Chen (3 shared papers)Padhraic Smyth (5 shared papers)Qi Su (1 shared paper)Jyh-Herng Chow (1 shared paper)Darya Chudova (1 shared paper)Heikki Mannila (2 shared papers)
- Journals
- Journal of Cheminformatics (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesRussiaUnited Kingdom
In The Last Decade
Dmitry Pavlov
16 papers receiving 446 citations
Peers
Comparison fields: 5 of 65
- Computer Science Applications 74
- Information Systems 254
- Artificial Intelligence 250
- Marketing 63
- Signal Processing 58
Countries citing papers authored by Dmitry Pavlov
This map shows the geographic impact of Dmitry Pavlov'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 Dmitry Pavlov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmitry Pavlov more than expected).
Fields of papers citing papers by Dmitry Pavlov
This network shows the impact of papers produced by Dmitry Pavlov. 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 Dmitry Pavlov. The network helps show where Dmitry Pavlov may publish in the future.
Co-authors
The 16 scholars most cited alongside Dmitry Pavlov, 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 | 2009 | 121 | |
| 2 | 2007 | 91 | |
| 3 | A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains | 2002 | 91 |
| 4 | 2000 | 37 | |
| 5 | Factor Modeling for Advertisement Targeting | 2009 | 24 |
| 6 | 2004 | 21 | |
| 7 | 2010 | 19 | |
| 8 | 1999 | 19 | |
| 9 | Mixtures of conditional maximum entropy models | 2003 | 14 |
| 10 | Winning The Transfer Learning Track of Yahoo!’s Learning To Rank Challenge with YetiRank | 2010 | 14 |
| 11 | 2010 | 10 | |
| 12 | 2001 | 8 | |
| 13 | 2013 | 7 | |
| 14 | 2010 | 5 | |
| 15 | 2009 | 4 | |
| 16 | 2003 | 2 |
About Dmitry Pavlov
Dmitry Pavlov is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Signal Processing and Computer Networks and Communications, having authored 16 papers that have together received 487 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (5 papers), Data Stream Mining Techniques (4 papers), Data Mining Algorithms and Applications (4 papers), Machine Learning and Data Classification (3 papers), Data Management and Algorithms (2 papers), Text and Document Classification Technologies (2 papers), Consumer Market Behavior and Pricing (2 papers) and Image and Video Quality Assessment (2 papers). The work is most often cited by research in Computer Science Applications (74 citations), Information Systems (254 citations), Artificial Intelligence (250 citations), Marketing (63 citations) and Signal Processing (58 citations). Dmitry Pavlov has collaborated with scholars based in United States, Russia and United Kingdom. Frequent co-authors include David M. Pennock, John Canny, Ye Chen, Padhraic Smyth, Qi Su, Jyh-Herng Chow, Darya Chudova, Heikki Mannila, Michael Kapralov and Cliff Brunk. Their work appears in journals such as Journal of Cheminformatics, ACM Transactions on Knowledge Discovery from Data, arXiv (Cornell University) and Neural Information Processing 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.