Pairwise Preference Modeling
Pairwise Preference Modeling is a methodological framework in information retrieval that, as a subset of preference learning, ranks items by systematically comparing pairs of documents to determine their relative relevance with respect to a query.
233
Publications
11.4K
Citations
595
Authors
295
Institutions
Publications per year
2017–2026
55
Parents
233
Label ranking by learning pairwise preferences
Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng et al. · Artificial Intelligence · 2008 · 536 citations
Pairwise preference regression for cold-start recommendation
Seung-Taek Park, Wei Chu · 2009 · 312 citations
Pairwise ranking aggregation in a crowdsourced setting
Xi Chen, Paul N. Bennett, Kevyn Collins‐Thompson et al. · 2013 · 307 citations
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295
University of Illinois Urbana-Champaign
Urbana, United States
10 h-index
20 publications
1.2K citations
University of South Florida
Tampa, United States
8 h-index
13 publications
641 citations
Nanyang Technological University
Singapore, Singapore
6 h-index
6 publications
232 citations

University of Toronto
Toronto, Canada
6 h-index
11 publications
420 citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
9 publications
776 citations
Shandong University
Jinan, China
4 h-index
8 publications
246 citations

University of Amsterdam
Amsterdam, The Netherlands
4 h-index
21 publications
827 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
4 h-index
7 publications
195 citations
1–8 of 295
Repository · 13 publications · 422 citations
11
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10 publications · 729 citations
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4 publications · 81 citations
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4 publications · 200 citations
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4 publications · 327 citations
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