Interactive Relevance Feedback
Interactive Relevance Feedback is a relevance feedback paradigm in which users iteratively refine information retrieval results through explicit, real-time interactions that directly inform subsequent query modifications or system adaptations.
101
Publications
8.6K
Citations
279
Authors
113
Institutions
Publications per year
2017–2026
12
Parents
101
Support vector machine active learning for image retrieval
Simon Tong, Edward Yi Chang · 2001 · 1.3K citations
Donna Harman · 1992 · 407 citations · Full text
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113

University of Massachusetts Amherst
Amherst Center, United States
8 h-index
11 publications
602 citations

University of Amsterdam
Amsterdam, The Netherlands
4 h-index
10 publications
192 citations
Stanford University
Stanford, United States
4 h-index
6 publications
1.9K citations
University of Illinois Urbana-Champaign
Urbana, United States
4 h-index
12 publications
7.7K citations

University of Glasgow
Glasgow, United Kingdom
3 h-index
8 publications
297 citations

IT University of Copenhagen
Copenhagen, Denmark
3 h-index
7 publications
116 citations
National University of Singapore
Singapore, Singapore
3 h-index
11 publications
298 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
3 h-index
7 publications
124 citations
1–8 of 113
3 publications · 85 citations
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3 publications · 1.8K citations
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JASIS · 2 publications · 143 citations
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2 publications · 32 citations
2
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2 publications · 260 citations
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