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A Novel Privacy-Preserved Recommender System Framework based on Federated Learning
[article]
2020
arXiv
pre-print
Recommender System (RS) is currently an effective way to solve information overload. To meet users' next click behavior, RS needs to collect users' personal information and behavior to achieve a comprehensive and profound user preference perception. However, these centrally collected data are privacy-sensitive, and any leakage may cause severe problems to both users and service providers. This paper proposed a novel privacy-preserved recommender system framework (PPRSF), through the application
arXiv:2011.05614v1
fatcat:rpb227km3bb3dhr3hwjj6rgv5q