Document Relevance Modeling
Document Relevance Modeling is a probabilistic framework that characterizes the likelihood of a document satisfying an information need given a query, distinguished from other retrieval models by its foundational reliance on probabilistic assumptions about relevance to estimate the probability of relevance for ranking.
304
Publications
20.6K
Citations
683
Authors
269
Institutions
Publications per year
2017–2026
52
Parents
304
Learning deep structured semantic models for web search using clickthrough data
Po-Sen Huang, Xiaodong He, Jianfeng Gao et al. · 2013 · 2.1K citations
An experimental comparison of click position-bias models
Nick Craswell, Onno Zoeter, Michael Taylor et al. · 2008 · 955 citations
On Relevance, Probabilistic Indexing and Information Retrieval
M. E. Maron, J. L. Kuhns · Journal of the ACM · 1960 · 901 citations · Full text
On newspaper headlines as relevance optimizers
Daniel Dor · Journal of Pragmatics · 2003 · 534 citations
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269
Microsoft (United States)
Redmond, United States
19 h-index
60 publications
18.5K citations
Yahoo (United States)
Sunnyvale, United States
15 h-index
50 publications
3.6K citations
Carnegie Mellon University
Pittsburgh, United States
13 h-index
24 publications
2.3K citations

University of Massachusetts Amherst
Amherst Center, United States
12 h-index
22 publications
935 citations
University of Illinois Urbana-Champaign
Urbana, United States
11 h-index
19 publications
3.2K citations

University of Amsterdam
Amsterdam, The Netherlands
11 h-index
20 publications
532 citations

University of Glasgow
Glasgow, United Kingdom
10 h-index
20 publications
754 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
9 h-index
17 publications
4K citations
1–8 of 269
13 publications · 1.5K citations
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8 publications · 169 citations
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JASIST · 6 publications · 98 citations
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JASIS · 5 publications · 155 citations
5
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Repository · 4 publications · 150 citations
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