Fedor Borisyuk
Impact in
- Information Systems top 5%
- Information Retrieval and Search Behavior
- Recommender Systems and Techniques
- Web Data Mining and Analysis
- Expert finding and Q&A systems
- Computer Science Applications top 10%
- Mobile Crowdsensing and Crowdsourcing
Papers in
-
- Topic Modeling 4
- Text and Document Classification Technologies 3
- Advanced Graph Neural Networks 2
- Domain Adaptation and Few-Shot Learning 1
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- Recommender Systems and Techniques 5
- Co-authors
- Peter Bailey (1 shared paper)Paul N. Bennett (1 shared paper)Susan Dumais (1 shared paper)Xiao‐Yuan Cui (1 shared paper)Wei Chu (1 shared paper)Ryen W. White (1 shared paper)Krishnaram Kenthapadi (2 shared papers)Liang Zhang (1 shared paper)
- Journals
- Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (1 paper)
- Partner nations
- United States
In The Last Decade
Fedor Borisyuk
8 papers receiving 277 citations
Peers
Comparison fields: 5 of 43
- Information Systems 217
- Computer Science Applications 24
- Artificial Intelligence 133
- Computer Vision and Pattern Recognition 72
- Management Science and Operations Research 35
Countries citing papers authored by Fedor Borisyuk
This map shows the geographic impact of Fedor Borisyuk'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 Fedor Borisyuk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fedor Borisyuk more than expected).
Fields of papers citing papers by Fedor Borisyuk
This network shows the impact of papers produced by Fedor Borisyuk. 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 Fedor Borisyuk. The network helps show where Fedor Borisyuk may publish in the future.
Co-authors
The 25 scholars most cited alongside Fedor Borisyuk, 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 | 2012 | 209 | |
| 2 | 2017 | 23 | |
| 3 | 2016 | 19 | |
| 4 | 2020 | 18 | |
| 5 | 2021 | 14 | |
| 6 | 2019 | 13 | |
| 7 | 2022 | 4 | |
| 8 | 2021 | 2 | |
| 9 | 2024 | 0 | |
| 10 | 2024 | 0 | |
| 11 | 2025 | 0 |
About Fedor Borisyuk
Fedor Borisyuk is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Information Systems and Management, having authored 11 papers that have together received 302 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (5 papers), Topic Modeling (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Image Retrieval and Classification Techniques (3 papers), Text and Document Classification Technologies (3 papers), Multimodal Machine Learning Applications (3 papers), Advanced Graph Neural Networks (2 papers) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Information Systems (217 citations), Computer Science Applications (24 citations), Artificial Intelligence (133 citations), Computer Vision and Pattern Recognition (72 citations) and Management Science and Operations Research (35 citations). Fedor Borisyuk has collaborated with scholars based in United States. Frequent co-authors include Peter Bailey, Paul N. Bennett, Susan Dumais, Xiao‐Yuan Cui, Wei Chu, Ryen W. White, Krishnaram Kenthapadi, Liang Zhang, David Stein and Bo Zhao. Their work appears in journals such as Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
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.