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Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback
[article]
2021
arXiv
pre-print
Recommender systems are commonly trained on centrally collected user interaction data like views or clicks. This practice however raises serious privacy concerns regarding the recommender's collection and handling of potentially sensitive data. Several privacy-aware recommender systems have been proposed in recent literature, but comparatively little attention has been given to systems at the intersection of implicit feedback and privacy. To address this shortcoming, we propose a practical
arXiv:2105.03941v3
fatcat:l3gpsrcitzeuxj6ahvrcnfckhe