  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.
