Robustness of Meta Matrix Factorization Against Strict Privacy Constraints

Peter Muellner*, Dominik Kowald, Elisabeth Lex

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

In this paper, we explore the reproducibility of MetaMF, a meta matrix factorization framework introduced by Lin et al. MetaMF employs meta learning for federated rating prediction to preserve users’ privacy. We reproduce the experiments of Lin et al. on five datasets, i.e., Douban, Hetrec-MovieLens, MovieLens 1M, Ciao, and Jester. Also, we study the impact of meta learning on the accuracy of MetaMF’s recommendations. Furthermore, in our work, we acknowledge that users may have different tolerances for revealing information about themselves. Hence, in a second strand of experiments, we investigate the robustness of MetaMF against strict privacy constraints. Our study illustrates that we can reproduce most of Lin et al.’s results. Plus, we provide strong evidence that meta learning is essential for MetaMF’s robustness against strict privacy constraints.

Originalspracheenglisch
TitelAdvances in Information Retrieval
Untertitel43rd European Conference on IR Research, ECIR 2021, Proceedings
Redakteure/-innenDjoerd Hiemstra, Marie-Francine Moens, Josiane Mothe, Raffaele Perego, Martin Potthast, Fabrizio Sebastiani
ErscheinungsortCham
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten107-119
Seitenumfang13
Band2
ISBN (Print)9783030722395
DOIs
PublikationsstatusVeröffentlicht - 2021
Veranstaltung43rd European Conference on Information Retrieval: ECIR 2021 - Virtuell
Dauer: 28 Mär 20211 Apr 2021

Publikationsreihe

NameLecture Notes in Computer Science
Band12657
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz43rd European Conference on Information Retrieval
KurztitelECIR 2021
OrtVirtuell
Zeitraum28/03/211/04/21

ASJC Scopus subject areas

  • !!Theoretical Computer Science
  • !!Computer Science(all)

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