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

• URL: http://arxiv.org/abs/2008.07192v4
• Date: Tue, 22 Dec 2020 10:14:40 GMT
• Title: How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
• Authors: Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci
• Abstract summary: 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.
• Score: 16.256897977543982
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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