User-based Collaborative Filtering
User-Based Collaborative Filtering is a collaborative filtering method that predicts a user's preferences by aggregating the opinions of users with similar past behavior, distinguished from item-based or model-based approaches by its reliance on user-user similarity computed from rating patterns.
571
Publications
43.5K
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1.5K
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Institutions
Publications per year
2017–2026
209
Variants
User-based Cf
Parents
571
An Algorithmic Framework for Performing Collaborative Filtering
Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers et al. · ACM SIGIR Forum · 2017 · 2.6K citations
An algorithmic framework for performing collaborative filtering
Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers et al. · 1999 · 2K citations · Full text
Exploiting geographical influence for collaborative point-of-interest recommendation
Mao Ye, Peifeng Yin, Wang-Chien Lee et al. · 2011 · 1.1K citations
Computing, Data Science, Data Mining +11
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University of Minnesota
Minneapolis, United States
10 h-index
32 publications
40.9K citations

Chinese Academy of Sciences
Beijing, China
9 h-index
16 publications
938 citations
Nanjing University
Nanjing, China
7 h-index
24 publications
1.9K citations

Universidad Politécnica de Madrid
Madrid, Spain
7 h-index
20 publications
2K citations

University of Electronic Science and Technology of China
Chengdu, China
7 h-index
14 publications
1.2K citations
Tsinghua University
Beijing, China
7 h-index
18 publications
1.2K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
13 publications
1.7K citations

Shanghai Jiao Tong University
Shanghai, China
6 h-index
15 publications
781 citations
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20 publications · 1.6K citations
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9 publications · 324 citations
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8 publications · 324 citations
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6 publications · 130 citations
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6 publications · 259 citations
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