Context-aware Matrix Factorization
Context-Aware Matrix Factorization is a matrix factorization framework that extends traditional collaborative filtering by incorporating contextual variables—such as user location, time, or device—into the latent factor model to enable personalized recommendations that dynamically adapt to situational influences.
63
Publications
3.6K
Citations
181
Authors
72
Institutions
Publications per year
2017–2026
29
63
Inferring gas consumption and pollution emission of vehicles throughout a city
Jingbo Shang, Yu Zheng, Wenzhu Tong et al. · 2014 · 252 citations
Context-based splitting of item ratings in collaborative filtering
Linas Baltrunas, Francesco Ricci⋆ · 2009 · 161 citations
Computing, Data Science, Data Mining +13
Collaborative QoS prediction with context-sensitive matrix factorization
Hao Wu, Kun Yue, Bo Li et al. · Future Generation Computer Systems · 2017 · 129 citations
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Free University of Bozen-Bolzano
Bolzano, Italy
6 h-index
12 publications
1.6K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
4 h-index
8 publications
458 citations

Budapest University of Technology and Economics
Budapest, Hungary
3 h-index
3 publications
115 citations

DePaul University
Chicago, United States
3 h-index
9 publications
360 citations
Microsoft Research Asia (China)
Beijing, China
2 h-index
4 publications
820 citations

Macquarie University
Sydney, Australia
2 h-index
2 publications
133 citations

University of Minnesota
Minneapolis, United States
2 h-index
5 publications
346 citations
Barcelona Digital Centro Tecnológico
Barcelona, Spain
2 h-index
2 publications
29 citations
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2 publications · 41 citations
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2 publications · 91 citations
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TIST · 2 publications · 187 citations
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FGCS · 2 publications · 183 citations
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2 publications · 172 citations
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