Relevance Feedback
Relevance Feedback is an interactive query reformulation technique in information retrieval that uses explicit or implicit assessments of the relevance of initially retrieved documents to modify the query representation—typically by reweighting and/or expanding its terms—in order to improve the ranking of subsequent retrieval results.
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Publications per year
2017–2026
396
2.5K
Optimizing search engines using clickthrough data
Thorsten Joachims · 2002 · 3.9K citations
Relevance feedback in information retrieval
J. J. Rocchio · Medical Entomology and Zoology · 1971 · 2.6K citations
Stephen Robertson, Steve Walker, Susan Jones et al. · Text REtrieval Conference · 1994 · 2.2K citations
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University of Illinois Urbana-Champaign, United States
31 publications · 2.2K citations
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Microsoft Research (United Kingdom), United Kingdom
28 publications · 5.4K citations
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Microsoft (United States)
Redmond, United States
54 h-index
262 publications
41.8K citations
University of Illinois Urbana-Champaign
Urbana, United States
41 h-index
141 publications
22.2K citations

University of Massachusetts Amherst
Amherst Center, United States
38 h-index
138 publications
13K citations
Microsoft Research Asia (China)
Beijing, China
36 h-index
147 publications
14.5K citations

Cornell University
Ithaca, United States
34 h-index
82 publications
27.3K citations
Tsinghua University
Beijing, China
31 h-index
206 publications
14.1K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
31 h-index
83 publications
12.5K citations
Yahoo (United States)
Sunnyvale, United States
30 h-index
147 publications
14.1K citations
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JASIS · 34 publications · 3.8K citations
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JASIST · 35 publications · 2.5K citations
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