Privacy-preserving Matrix Factorization
Privacy-Preserving Matrix Factorization is a computational technique within collaborative filtering-based recommendation systems that performs matrix factorization while enforcing privacy constraints to protect sensitive user data during model training and inference.
80
Publications
3.6K
Citations
263
Authors
135
Institutions
Publications per year
2017–2026
63
80
Privacy-preserving matrix factorization
Valeria Nikolaenko, Stratis Ioannidis, Udi Weinsberg et al. · 2013 · 283 citations
FedRec: Federated Recommendation With Explicit Feedback
Guanyu Lin, Feng Liang, Weike Pan et al. · IEEE Intelligent Systems · 2020 · 207 citations
Applying Differential Privacy to Matrix Factorization
Arnaud Berlioz, Arik Friedman, Mohamed Ali Kâafar et al. · 2015 · 119 citations
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Nanjing University of Science and Technology
Nanjing, China
5 h-index
15 publications
535 citations

Deakin University
Burwood, Australia
4 h-index
5 publications
317 citations
Chongqing University of Posts and Telecommunications
Chongqing, China
4 h-index
11 publications
376 citations
Samsung (South Korea)
Seoul, South Korea
4 h-index
12 publications
1.2K citations

Lanzhou University of Technology
Lanzhou, China
3 h-index
3 publications
109 citations
The University of Texas at San Antonio
San Antonio, United States
3 h-index
4 publications
144 citations
Shandong University
Jinan, China
3 h-index
8 publications
790 citations

Nanjing University of Information Science and Technology
Nanjing, China
3 h-index
6 publications
117 citations
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4 publications · 137 citations
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4 publications · 150 citations
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3 publications · 113 citations
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Repository · 3 publications · 45 citations
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2 publications · 95 citations
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