How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank

TL;DR

This paper introduces FPL, a federated learning architecture for top-N recommendation systems that allows users to control their sensitive data sharing while collaboratively training a ranking model.

Contribution

It proposes a novel federated learning approach for recommendation that emphasizes user data control and privacy preservation during model training.

Findings

Enables user-controlled data sharing in federated recommendation.

Utilizes pair-wise learning-to-rank optimization within federated learning.

Provides a publicly available implementation for practical use.

Abstract

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.