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

Proceedings of the 36th ACM SIGAPP Symposium On Applied Computing, SAC 2021, Gwangju, Korea (Virtual Event) - 2021
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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.

BibTex references


@InProceedings{ADDFN21,
author = {Vito Walter Anelli and Yashar Deldjoo and Tommaso {Di Noia} and Antonio Ferrara and Fedelucio Narducci},
title = "How to Put Users in Control of their Data in
Federated Top-N Recommendation with Learning to
Rank",
booktitle = "Proceedings of the 36th ACM SIGAPP Symposium On
Applied Computing, SAC 2021, Gwangju, Korea
(Virtual Event)",
year = "2021",
url = "http://www-ictserv.poliba.it/publications/2021/ADD
FN21"
}

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SisInf Lab - Information Systems Laboratory

Research group of Politecnico di Bari
Edoardo Orabona St, 4 Bari, Italy