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.

中文翻译：

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元矩阵分解对严格隐私约束的稳健性

在本文中，我们探讨了Lin等人介绍的MetaMF的可再现性。MetaMF使用元学习进行联合评级预测，以保护用户的隐私。我们复制林等人的实验。在五个数据集上，即豆瓣，赫特雷克-电影镜头，MovieLens 1M，Ciao和Jester。此外，我们研究了元学习对MetaMF建议准确性的影响。此外，在我们的工作中，我们承认用户在披露有关自己的信息时可能会有不同的容忍度。因此，在第二阶段的实验中，我们研究了MetaMF对严格的隐私约束的鲁棒性。我们的研究表明，我们可以重现Lin等人的大部分结果。另外，我们提供有力的证据表明，元学习对于MetaMF'至关重要