Bayesian Personalized Ranking
Bayesian Personalized Ranking is a pairwise preference learning framework for personalized item ranking that formulates the optimization problem as maximizing the posterior probability of observed user preferences under a Bayesian inference paradigm, distinguishing it from pointwise or regression-based collaborative filtering approaches by focusing on relative item comparisons rather than absolute ratings.
84
Publications
13.3K
Citations
291
Authors
96
Institutions
Publications per year
2017–2026
59
Parents
84
BPR: Bayesian Personalized Ranking from Implicit Feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner et al. · arXiv (Cornell University) · 2012 · 4.4K citations · Full text
Adversarial Personalized Ranking for Recommendation
Xiangnan He, Zhankui He, Xiaoyu Du et al. · 2018 · 401 citations · Full text
1–5 of 84
291
7
h-index
6
h-index
6
h-index
4
h-index
4
h-index
1–5 of 291
96
Tsinghua University
Beijing, China
7 h-index
21 publications
930 citations
University of Hildesheim
Hildesheim, Germany
6 h-index
20 publications
24.4K citations
University of Science and Technology of China
Hefei, China
5 h-index
20 publications
1.5K citations

Beijing Institute of Technology
Beijing, China
5 h-index
15 publications
1.3K citations

Zhejiang University
Hangzhou, China
4 h-index
9 publications
919 citations
National University of Singapore
Singapore, Singapore
4 h-index
7 publications
1.1K citations

Shenzhen University
Shenzhen, China
4 h-index
9 publications
538 citations

Northeastern University
Shenyang, China
3 h-index
4 publications
211 citations
1–8 of 96
Repository · 7 publications · 4.6K citations
7
h-index
4 publications · 83 citations
4
h-index
Conference · 3 publications · 1.1K citations
3
h-index
3 publications · 94 citations
3
h-index
3 publications · 174 citations
3
h-index
1–5