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.
CITATION STYLE
Muellner, P., Kowald, D., & Lex, E. (2021). Robustness of Meta Matrix Factorization Against Strict Privacy Constraints. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12657 LNCS, pp. 107–119). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72240-1_8
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