Probabilistic Relevance Estimation
Probabilistic Relevance Estimation is a formal framework in information retrieval that models the likelihood of a document being relevant to a query by computing probabilities based on evidence from document-query relationships, distinguishing itself from deterministic or heuristic approaches through its foundation in probabilistic inference.
51
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6.5K
Citations
122
Authors
55
Institutions
Publications per year
2017–2026
6
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51
Relevance-Based Language Models
Victor Lavrenko, W. Bruce Croft · ACM SIGIR Forum · 2017 · 1.4K citations
Relevance based language models
Victor Lavrenko, W. Bruce Croft · 2001 · 1K citations
A dynamic bayesian network click model for web search ranking
Olivier Chapelle, Ya Zhang · 2009 · 543 citations
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University of Massachusetts Amherst
Amherst Center, United States
6 h-index
16 publications
6.5K citations
Microsoft (United States)
Redmond, United States
5 h-index
10 publications
959 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
9 publications
907 citations
Centrum Wiskunde & Informatica
Amsterdam, The Netherlands
3 h-index
3 publications
122 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
9 publications
1.7K citations
University of California, Santa Cruz
Santa Cruz, United States
2 h-index
4 publications
88 citations
University College London
London, United Kingdom
2 h-index
3 publications
111 citations
Dalhousie University
Halifax, Canada
2 h-index
4 publications
134 citations
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JASIS · 2 publications · 87 citations
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Conference · 2 publications · 98 citations
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2 publications · 224 citations
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Repository · 1 publications · 12 citations
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1 publications · 28 citations
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