Robustness of Meta Matrix Factorization Against Strict Privacy Constraints

TL;DR

This paper evaluates the reproducibility and robustness of MetaMF, a meta learning-based federated matrix factorization framework, demonstrating its effectiveness under strict privacy constraints across multiple datasets.

Contribution

It reproduces prior results of MetaMF and shows that meta learning is crucial for maintaining recommendation accuracy under strict privacy constraints.

Findings

Most of Lin et al.'s results are reproducible.

Meta learning enhances robustness against privacy constraints.

MetaMF performs well across five datasets.

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.