Collaborative Filtering
Collaborative Filtering is a recommendation technique that predicts a user's preferences by leveraging the collective behavior of other users, distinguishing itself through its reliance on user-item interaction patterns rather than content-based features.
8.3K
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633.1K
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17.5K
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3.1K
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Publications per year
2017–2026
4.2K
Parents
8.3K
Matrix Factorization Techniques for Recommender Systems
Yehuda Koren, Robert Bell, Chris Volinsky · Computer · 2009 · 11.7K citations
Item-based collaborative filtering recommendation algorithms
Badrul Sarwar, George Karypis, Joseph A. Konstan et al. · 2001 · 9K citations
Neural Collaborative Filtering
Xiangnan He, Lizi Liao, Hanwang Zhang et al. · 2017 · 6.6K citations · Full text
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University of Minnesota
Minneapolis, United States
63 h-index
265 publications
186.1K citations
University of Science and Technology of China
Hefei, China
61 h-index
370 publications
48.6K citations
Tsinghua University
Beijing, China
58 h-index
432 publications
42K citations
National University of Singapore
Singapore, Singapore
51 h-index
198 publications
51.6K citations

Chinese Academy of Sciences
Beijing, China
46 h-index
284 publications
16.5K citations
University of Technology Sydney
Sydney, Australia
46 h-index
238 publications
23.5K citations
Carnegie Mellon University
Pittsburgh, United States
45 h-index
141 publications
14.8K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
45 h-index
171 publications
24.8K citations
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336 publications · 22.3K citations
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208 publications · 14.3K citations
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163 publications · 10K citations
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Repository · 220 publications · 16.9K citations
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TKDE · 90 publications · 19.6K citations
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