Probabilistic Retrieval
Probabilistic Retrieval is an information retrieval framework that ranks documents based on the estimated probability of relevance to a given query, distinguishing itself from other retrieval models by employing probabilistic theories to quantify uncertainty in document relevance.
1.1K
Publications
100.5K
Citations
2K
Authors
566
Institutions
Publications per year
2017–2026
88
Variants
Probabilistic Information Retrieval
Parents
1.1K
Probabilistic Latent Semantic Indexing
Thomas Hofmann · ACM SIGIR Forum · 2017 · 4.1K citations
Probabilistic latent semantic indexing
Thomas Hofmann · 1999 · 3.9K citations · Full text
Zhe Cao, Tao Qin, Tie‐Yan Liu et al. · 2007 · 2K citations
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2K
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Microsoft Research (United Kingdom), United Kingdom
24 publications · 10.2K citations
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University of Illinois Urbana-Champaign, United States
17 publications · 3.1K citations
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14
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566

University of Massachusetts Amherst
Amherst Center, United States
34 h-index
88 publications
25.7K citations
Microsoft (United States)
Redmond, United States
26 h-index
73 publications
8.7K citations
Carnegie Mellon University
Pittsburgh, United States
24 h-index
67 publications
7.7K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
22 h-index
37 publications
10.5K citations
University of Illinois Urbana-Champaign
Urbana, United States
21 h-index
59 publications
6.5K citations
University of California, Berkeley
Berkeley, United States
20 h-index
32 publications
12.7K citations
Microsoft Research Asia (China)
Beijing, China
20 h-index
69 publications
10.5K citations
Tsinghua University
Beijing, China
19 h-index
50 publications
8.3K citations
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36 publications · 4.8K citations
27
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43 publications · 3.6K citations
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JASIS · 37 publications · 3.8K citations
22
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14 publications · 7.9K citations
13
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TKDE · 12 publications · 713 citations
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