A Novel Privacy-Preserved Recommender System Framework based on Federated Learning
This addresses privacy concerns for users and service providers in recommender systems, though it is incremental as it applies an existing paradigm to a specific domain.
The paper tackles the privacy risks in recommender systems by proposing a privacy-preserved framework based on federated learning, which trains and infers without centrally collecting user data, reducing leakage risk and meeting legal requirements.
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 of federated learning paradigm, to enable the recommendation algorithm to be trained and carry out inference without centrally collecting users' private data. The PPRSF not only able to reduces the privacy leakage risk, satisfies legal and regulatory requirements but also allows various recommendation algorithms to be applied.