  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.
