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.

翻译：在本文中,我们探讨了MetMF的可复制性,MetMF是Lin等人提出的一个元矩阵要素化框架。MetMF利用元学习来进行联合评级预测,以保护用户的隐私。我们复制了Lin等人在五个数据集上的实验,即Douban、Hetrec-MovieLens、MovieLens 1M、Ciao和Jester。此外,我们研究了元学习对MetMF建议的准确性的影响。此外,我们在工作中承认,用户在披露自身信息方面可能有不同的容忍度。因此,在第二层实验中,我们研究了MetMF在严格的隐私限制方面的强健性。我们的研究显示,我们可以复制Lin等人的大部分结果。此外,我们提供了有力的证据,即元学习对于MetMF在严格的隐私限制下保持稳健性至关重要。