Explicit Relevance Feedback is a type of relevance feedback in information retrieval where users directly and consciously provide judgments—such as relevance ratings or binary classifications—on retrieved documents, distinguishing it from implicit methods that infer relevance from user behavior.
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3.8K
Citations
222
Authors
105
Institutions
Publications per year
2017–2026
12
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72
To personalize or not to personalize
Jaime Teevan, Susan Dumais, Daniel J. Liebling · 2008 · 252 citations
On the use of relevance feedback in IR-based concept location
Gregory Gay, Sonia Haiduc, Andrian Marcus et al. · 2009 · 184 citations · Full text
Relevance feedback and other query modification techniques
Donna Harman · Information Retrieval · 1992 · 155 citations
1–5 of 72
Authors
222
- JT
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1–5 of 222
Institutions
105
Microsoft (United States)
Redmond, United States
3 h-index
9 publications
1.2K citations
Tokyo Institute of Technology
Tokyo, Japan
2 h-index
5 publications
98 citations
University of Pittsburgh
Pittsburgh, United States
2 h-index
6 publications
362 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
3 publications
49 citations
Université de Poitiers
Poitiers, France
2 h-index
3 publications
264 citations
Istituto di Matematica Applicata e Tecnologie Informatiche
Pavia, Italy
2 h-index
5 publications
237 citations
City University of Hong Kong
Hong Kong, Hong Kong
2 h-index
2 publications
35 citations
Hong Kong University of Science and Technology
Hong Kong, Hong Kong
2 h-index
2 publications
35 citations
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Venues
- Information Processing & Management
11 publications · 433 citations
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- Information Sciences
2 publications · 60 citations
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- ACM SIGIR Forum
2 publications · 45 citations
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- Neurocomputing
2 publications · 34 citations
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- ACM Multimedia
Conference · 1 publications · 39 citations
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