Recommendation Systems
Recommendation Systems is an information-filtering and decision-support methodology in customer analytics, data mining, e-commerce systems, and machine learning that models users and items from observed data to predict and rank unseen items by their expected relevance or utility for individual users, thereby generating personalized content or product suggestions at scale.
13.3K
Publications
908.4K
Citations
25.9K
Authors
3.9K
Institutions
Publications per year
2017–2026
6.8K
13.3K
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
1–5 of 13.3K
25.9K
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43
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1–5 of 25.9K
3.9K
Tsinghua University
Beijing, China
79 h-index
874 publications
70.6K citations

University of Minnesota
Minneapolis, United States
78 h-index
394 publications
177.1K citations
University of Science and Technology of China
Hefei, China
72 h-index
512 publications
59.5K citations
National University of Singapore
Singapore, Singapore
64 h-index
292 publications
70.4K citations
University of Technology Sydney
Sydney, Australia
60 h-index
379 publications
31.6K citations

Chinese Academy of Sciences
Beijing, China
58 h-index
465 publications
32.5K citations
Nanyang Technological University
Singapore, Singapore
57 h-index
323 publications
30.8K citations

University of Electronic Science and Technology of China
Chengdu, China
54 h-index
378 publications
27.4K citations
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381 publications · 26.6K citations
90
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202 publications · 15K citations
69
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Repository · 379 publications · 28.4K citations
68
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TKDE · 164 publications · 25K citations
62
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Conference · 144 publications · 12.7K citations
57
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