Bayesian Collaborative Filtering
Bayesian Collaborative Filtering is a probabilistic collaborative filtering method that employs Bayesian inference to model user-item interactions by incorporating prior distributions over latent factors and updating them based on observed preferences, thereby enabling uncertainty-aware predictions and personalized recommendations.
84
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8.2K
Citations
265
Authors
128
Institutions
Publications per year
2017–2026
33
Parents
84
Personalized news recommendation based on click behavior
Jiahui Liu, P. Dolan, Elin Rønby Pedersen · 2010 · 764 citations
Internet Recommendation Systems
Asim Ansari, Skander Essegaier, Rajeev Kohli · Journal of Marketing Research · 2000 · 718 citations
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
Liang Xiong, Xi Chen, Tzu-Kuo Huang et al. · 2010 · 590 citations · Full text
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128

Shenzhen University
Shenzhen, China
4 h-index
10 publications
313 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
13 publications
3.4K citations

Universidad Politécnica de Madrid
Madrid, Spain
3 h-index
9 publications
1.2K citations
University of Science and Technology of China
Hefei, China
3 h-index
7 publications
372 citations
Singapore Management University
Singapore, Singapore
3 h-index
5 publications
302 citations

Sun Yat-sen University
Guangzhou, China
3 h-index
11 publications
1.4K citations
University of Cambridge
Cambridge, United Kingdom
3 h-index
3 publications
456 citations
Microsoft (United States)
Redmond, United States
2 h-index
5 publications
154 citations
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Conference · 4 publications · 127 citations
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2 publications · 51 citations
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2 publications · 324 citations
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Repository · 2 publications · 114 citations
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2 publications · 120 citations
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