Pairwise Ranking
Pairwise Ranking is a relevance ranking method that determines the relative order of items by comparing pairs of user-item interactions to optimize the likelihood that a more relevant item is ranked higher than a less relevant one.
233
Publications
27.8K
Citations
683
Authors
311
Institutions
Publications per year
2017–2026
95
233
Individual Comparisons by Ranking Methods
Frank Wilcoxon · Biometrics Bulletin · 1945 · 14.4K citations
Business, Computer Science, Multi-criteria Decision Making +8
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
Chenyan Xiong, Zhuyun Dai, Jamie Callan et al. · 2017 · 572 citations · Full text
Label ranking by learning pairwise preferences
Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng et al. · Artificial Intelligence · 2008 · 536 citations
1–5 of 233
683
5
h-index
4
h-index
4
h-index
4
h-index
3
h-index
1–5 of 683
311
Tsinghua University
Beijing, China
9 h-index
23 publications
1.9K citations
Microsoft (United States)
Redmond, United States
6 h-index
11 publications
931 citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
19 publications
2.7K citations

Chinese Academy of Sciences
Beijing, China
6 h-index
16 publications
388 citations
HUN-REN Institute for Computer Science and Control
Budapest, Hungary
5 h-index
6 publications
273 citations
Stanford University
Stanford, United States
5 h-index
9 publications
1.4K citations

Corvinus University of Budapest
Budapest, Hungary
5 h-index
9 publications
571 citations
University of Chinese Academy of Sciences
Beijing, China
5 h-index
15 publications
452 citations
1–8 of 311
Repository · 19 publications · 685 citations
13
h-index
5 publications · 160 citations
5
h-index
Conference · 4 publications · 172 citations
4
h-index
4 publications · 346 citations
4
h-index
4 publications · 214 citations
4
h-index
1–5