Implicit Relevance Feedback is a relevance feedback method that infers user preferences from indirect user behavior, such as dwell time or click patterns, without requiring explicit judgments of document relevance.
52
Publications
2.1K
Citations
162
Authors
65
Institutions
Publications per year
2017–2026
10
Parents
Publications
52
Dataset-driven research for improving recommender systems for learning
Katrien Verbert, Hendrik Drachsler, Nikos Manouselis et al. · 2011 · 171 citations · Full text
Georg Buscher, Andreas Dengel, Ralf Biedert et al. · ACM Transactions on Interactive Intelligent Systems · 2012 · 113 citations
1–5 of 52
Authors
162
- SM
7
h-index
- MZ
6
h-index
- YL
5
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- RW
4
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- JM
4
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1–5 of 162
Institutions
65
Tsinghua University
Beijing, China
8 h-index
40 publications
1.3K citations
Microsoft Research Asia (China)
Beijing, China
6 h-index
9 publications
184 citations
Microsoft (United States)
Redmond, United States
6 h-index
8 publications
363 citations
German Research Centre for Artificial Intelligence
Kaiserslautern, Germany
3 h-index
10 publications
665 citations
Aalto University
Espoo, Finland
3 h-index
4 publications
113 citations
University of Glasgow
Glasgow, United Kingdom
3 h-index
6 publications
425 citations
Université de Montréal
Montreal, Canada
2 h-index
2 publications
79 citations
University of Catania
Catania, Italy
2 h-index
8 publications
204 citations
1–8 of 65
Venues
- IEEE Transactions on Image Processing
3 publications · 92 citations
3
h-index
- ACM Transactions on Information Systems
2 publications · 43 citations
2
h-index
- Journal of the American Society for Information Science and Technology
JASIST · 2 publications · 28 citations
2
h-index
- International ACM SIGIR Conference on Research and Development in Information Retrieval
Conference · 1 publications · 11 citations
1
h-index
- Journal of the Association for Information Science and Technology
JASIST · 1 publications · 33 citations
1
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1–5