Pseudo-relevance Feedback
Pseudo-Relevance Feedback is a query expansion technique in information retrieval where the top-ranked documents from an initial retrieval are automatically assumed to be relevant and used to augment the original query without explicit user relevance judgments.
214
Publications
10.1K
Citations
510
Authors
204
Institutions
Publications per year
2017–2026
51
214
Generating query substitutions
Rosie Jones, Benjamin Rey, Omid Madani et al. · 2006 · 620 citations
Selecting good expansion terms for pseudo-relevance feedback
Guihong Cao, Jian‐Yun Nie, Jianfeng Gao et al. · 2008 · 434 citations
Improving pseudo-relevance feedback in web information retrieval using web page segmentation
Shipeng Yu, Deng Cai, Ji-Rong Wen et al. · 2003 · 251 citations
Latent concept expansion using markov random fields
Donald Metzler, W. Bruce Croft · 2007 · 228 citations
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204

University of Massachusetts Amherst
Amherst Center, United States
16 h-index
38 publications
2.7K citations

York University
Toronto, Canada
10 h-index
30 publications
1.1K citations
Carnegie Mellon University
Pittsburgh, United States
10 h-index
27 publications
1.9K citations

National Taiwan University
Taipei, Taiwan
9 h-index
27 publications
458 citations
University of Illinois Urbana-Champaign
Urbana, United States
8 h-index
14 publications
1.3K citations

University of Glasgow
Glasgow, United Kingdom
8 h-index
20 publications
1.3K citations
Technion – Israel Institute of Technology
Haifa, Israel
7 h-index
14 publications
867 citations
Microsoft (United States)
Redmond, United States
7 h-index
9 publications
767 citations
1–8 of 204
Conference · 6 publications · 81 citations
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JASIST · 6 publications · 108 citations
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JIS · 5 publications · 79 citations
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5 publications · 161 citations
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4 publications · 119 citations
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