Probabilistic Relevance Feedback
Probabilistic Relevance Feedback is a relevance feedback method that relies on probabilistic models to estimate the likelihood of document relevance based on user feedback, thereby refining information retrieval through statistical inference.
65
Publications
4.9K
Citations
133
Authors
76
Institutions
Publications per year
2017–2026
4
Parents
65
PicHunter: Bayesian relevance feedback for image retrieval
Ingemar J. Cox, Matthew L. Miller, S.M. Omohundro et al. · 1996 · 215 citations
Positional relevance model for pseudo-relevance feedback
Yuanhua Lv, ChengXiang Zhai · 2010 · 214 citations
Microsoft Cambridge at TREC 13: Web and Hard Tracks.
Hugo Zaragoza, Nick Craswell, Michael Taylor et al. · 2004 · 149 citations
1–5 of 65
133
4
h-index
4
h-index
4
h-index
3
h-index
3
h-index
1–5 of 133
76
Princeton University
Princeton, United States
4 h-index
14 publications
3.5K citations

Université de Sherbrooke
Sherbrooke, Canada
3 h-index
5 publications
152 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
3 h-index
6 publications
290 citations

University of Glasgow
Glasgow, United Kingdom
2 h-index
4 publications
141 citations
Massachusetts Institute of Technology
Cambridge, United States
2 h-index
3 publications
829 citations
New York University
New York, United States
2 h-index
3 publications
77 citations
University of California, Santa Cruz
Santa Cruz, United States
2 h-index
4 publications
106 citations
National Technical University of Athens
Athens, Greece
2 h-index
4 publications
176 citations
1–8 of 76
3 publications · 278 citations
3
h-index
3 publications · 838 citations
3
h-index
3 publications · 144 citations
3
h-index
2 publications · 67 citations
2
h-index
JASIST · 2 publications · 28 citations
2
h-index
1–5