TY - GEN
T1 - Robustness of Meta Matrix Factorization Against Strict Privacy Constraints
AU - Muellner, Peter
AU - Kowald, Dominik
AU - Lex, Elisabeth
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Federated learning
KW - Matrix factorization
KW - Meta learning
KW - Privacy
KW - Recommender systems
KW - Reproducibility
UR - http://www.scopus.com/inward/record.url?scp=85107325973&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72240-1_8
DO - 10.1007/978-3-030-72240-1_8
M3 - Conference paper
AN - SCOPUS:85107325973
SN - 9783030722395
VL - 2
T3 - Lecture Notes in Computer Science
SP - 107
EP - 119
BT - Advances in Information Retrieval
A2 - Hiemstra, Djoerd
A2 - Moens, Marie-Francine
A2 - Mothe, Josiane
A2 - Perego, Raffaele
A2 - Potthast, Martin
A2 - Sebastiani, Fabrizio
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 43rd European Conference on Information Retrieval
Y2 - 28 March 2021 through 1 April 2021
ER -