How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
This addresses privacy concerns for users in federated recommendation systems, though it is incremental as it builds on existing federated learning principles.
The paper tackles the privacy issue in recommendation systems by proposing FPL, a federated learning architecture that allows users to control sensitive data sharing while training a central factorization model, achieving competitive performance with state-of-the-art methods.
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, data ownership is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations) with a central server. Unfortunately, data harvesting and collection is at the basis of modern, state-of-the-art approaches to recommendation. To address this issue, we present FPL, an architecture in which users collaborate in training a central factorization model while controlling the amount of sensitive data leaving their devices. The proposed approach implements pair-wise learning-to-rank optimization by following the Federated Learning principles, originally conceived to mitigate the privacy risks of traditional machine learning. The public implementation is available at https://split.to/sisinflab-fpl.