Pairwise Ranking Models
Pairwise Ranking Models is a retrieval framework that ranks documents by learning to predict relative preferences between pairs of documents with respect to a given query, distinguishing it from pointwise or listwise approaches by focusing on pairwise comparisons to optimize ranking performance.
227
Publications
16.6K
Citations
651
Authors
265
Institutions
Publications per year
2017–2026
100
Variants
Pairwise Ranking Frameworks
Parents
227
Multiple Comparisons Using Rank Sums
Olive Jean Dunn · Technometrics · 1964 · 4.4K citations
Multiple Comparisons Using Rank Sums
Olive Jean Dunn · Technometrics · 1964 · 842 citations
Statistical ranking and combinatorial Hodge theory
Xiaoye Jiang, Lek‐Heng Lim, Yuan Yao et al. · Mathematical Programming · 2010 · 338 citations · Full text
Pairwise ranking aggregation in a crowdsourced setting
Xi Chen, Paul N. Bennett, Kevyn Collins‐Thompson et al. · 2013 · 307 citations
1–5 of 227
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265
Tsinghua University
Beijing, China
9 h-index
28 publications
1.7K citations
Stanford University
Stanford, United States
7 h-index
14 publications
1.5K citations
Microsoft Research Asia (China)
Beijing, China
6 h-index
11 publications
1.3K citations

Chinese Academy of Sciences
Beijing, China
6 h-index
16 publications
388 citations
Yahoo (United States)
Sunnyvale, United States
6 h-index
18 publications
714 citations

Peking University
Beijing, China
6 h-index
15 publications
1.3K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
6 h-index
11 publications
1.1K citations

Shanghai Jiao Tong University
Shanghai, China
5 h-index
9 publications
457 citations
1–8 of 265
Repository · 19 publications · 746 citations
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6 publications · 305 citations
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4 publications · 135 citations
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4 publications · 142 citations
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2 publications · 105 citations
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