Robustness of Meta Matrix Factorization Against Strict Privacy Constraints
This work addresses privacy preservation in federated recommendation systems, but it is incremental as it focuses on reproducing and validating an existing method.
The paper reproduces MetaMF, a meta matrix factorization framework for federated rating prediction, on five datasets and finds that meta learning is crucial for its robustness under strict privacy constraints.
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.