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Robustness of Meta Matrix Factorization Against Strict Privacy Constraints

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12657)

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.

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Notes

  1. 1.

    https://bitbucket.org/HeavenDog/metamf/src/master/, Last accessed Oct. 2020.

  2. 2.

    https://github.com/pmuellner/RobustnessOfMetaMF.

  3. 3.

    https://doi.org/10.5281/zenodo.4031011.

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Acknowledgements

We thank the Social Computing team for their rich feedback on this work. This work is supported by the H2020 project TRUSTS (GA: 871481) and the “DDAI” COMET Module within the COMET – Competence Centers for Excellent Technologies Programme, funded by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit), the Austrian Federal Ministry for Digital and Economic Affairs (bmdw), the Austrian Research Promotion Agency (FFG), the province of Styria (SFG) and partners from industry and academia. The COMET Programme is managed by FFG.

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Correspondence to Peter Muellner .

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Muellner, P., Kowald, D., Lex, E. (2021). Robustness of Meta Matrix Factorization Against Strict Privacy Constraints. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-72240-1_8

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