Addressing Privacy in Recommender Systems with Federated Learning

In recent years, recommender systems have successfully assisted user decision-making in various user-centered applications. In such scenarios, the modern approaches are based on collecting user-sensitive preferences. However, data collection is crucial since users now worry about the related privacy risks when sharing their data. This work presents a recommendation approach based on the Federated Learning paradigm, a distributed privacy-preserving approach to the recommendation. Here, users collaborate on the training while still controlling the amount of the shared sensitive data. This paper presents FPL: a pair-wise learning-to-rank approach based on Federated Learning. We show that it puts users in control of their data and reveals recommendation performance competing with centralized state-of-the-art approaches. The public implementation is available at https://split.to/sisinflab-fpl.