Pairwise Ranking Optimization
Pairwise Ranking Optimization is a machine learning methodology that, within the context of implicit feedback modeling, optimizes the relative ordering of user-item interactions by minimizing a loss function defined over pairs of observed and unobserved preferences, thereby distinguishing itself from pointwise approaches through its focus on comparative, rather than absolute, relevance predictions.
30
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2K
Citations
116
Authors
50
Institutions
Publications per year
2017–2026
19
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30
Adversarial Personalized Ranking for Recommendation
Xiangnan He, Zhankui He, Xiaoyu Du et al. · 2018 · 401 citations · Full text
Mark Hopkins, Jonathan May · 2011 · 246 citations
Exploiting Multi-grain Ranking Constraints for Precisely Searching Visually-similar Vehicles
Ke Yan, Yonghong Tian, Yaowei Wang et al. · 2017 · 146 citations
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Zhen Qin, Rolf Jagerman, Kai Hui et al. · 2024 · 121 citations · Full text
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National University of Singapore
Singapore, Singapore
4 h-index
6 publications
1.1K citations

Shanghai Jiao Tong University
Shanghai, China
3 h-index
9 publications
265 citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
10 publications
261 citations
University of Virginia
Charlottesville, United States
2 h-index
5 publications
106 citations
Tsinghua University
Beijing, China
2 h-index
8 publications
596 citations
Peng Cheng Laboratory
Shenzhen, China
2 h-index
5 publications
96 citations
University of Science and Technology of China
Hefei, China
2 h-index
2 publications
85 citations
Google (United States)
Mountain View, United States
2 h-index
14 publications
1.5K citations
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Repository · 2 publications · 29 citations
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OR · 1 publications · 11 citations
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1 publications · 10 citations
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TIST · 1 publications · 11 citations
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1 publications · 34 citations
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