Probabilistic Retrieval Models is a retrieval framework that ranks documents based on the estimated probability of relevance to a query, distinguishing itself from other retrieval models by employing probabilistic inference to quantify uncertainty and optimize retrieval effectiveness.
489
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46.9K
Citations
814
Authors
287
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Publications per year
2017–2026
26
Publications
489
Relevance-Based Language Models
Victor Lavrenko, W. Bruce Croft · ACM SIGIR Forum · 2017 · 1.4K citations
THE PROBABILITY RANKING PRINCIPLE IN IR
Stephen Robertson · Journal of Documentation · 1977 · 1.1K citations
1–5 of 489
Authors
814
- WB
23
h-index
- NF
16
h-index
- SR
15
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- CZChengXiang Zhai
University of Illinois Urbana-Champaign, United States
11 publications · 2.9K citations
11
h-index
- DH
10
h-index
1–5 of 814
Institutions
287
University of Massachusetts Amherst
Amherst Center, United States
24 h-index
44 publications
11.1K citations
Carnegie Mellon University
Pittsburgh, United States
20 h-index
45 publications
8K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
15 h-index
22 publications
7K citations
Microsoft (United States)
Redmond, United States
14 h-index
24 publications
1.7K citations
University of Glasgow
Glasgow, United Kingdom
12 h-index
23 publications
2.2K citations
University College London
London, United Kingdom
11 h-index
16 publications
3.7K citations
York University
Toronto, Canada
10 h-index
26 publications
1.1K citations
University of California, Berkeley
Berkeley, United States
10 h-index
12 publications
691 citations
1–8 of 287
Venues
- Information Processing & Management
35 publications · 2.9K citations
22
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- Journal of the American Society for Information Science
JASIS · 30 publications · 3.3K citations
20
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- ACM Transactions on Information Systems
17 publications · 2.6K citations
17
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- Information Retrieval
9 publications · 559 citations
9
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- ACM SIGIR Forum
8 publications · 2.8K citations
8
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