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
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https://bitbucket.org/HeavenDog/metamf/src/master/, Last accessed Oct. 2020.
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References
Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The unfairness of popularity bias in recommendation. In: Workshop on Recommendation in Multistakeholder Environments in Conjunction with RecSys 2019 (2019)
Ammad-Ud-Din, M., et al.: Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888 (2019)
Cantador, I., Brusilovsky, P., Kuflik, T.: Second international workshop on information heterogeneity and fusion in recommender systems. In: RecSys 2011 (2011)
Chen, C., Zhang, J., Tung, A.K., Kankanhalli, M., Chen, G.: Robust federated recommendation system. arXiv preprint arXiv:2006.08259 (2020)
Chen, F., Luo, M., Dong, Z., Li, Z., He, X.: Federated meta-learning with fast convergence and efficient communication. arXiv preprint arXiv:1802.07876 (2018)
Duriakova, E., et al.: PDMFRec: a decentralised matrix factorisation with tunable user-centric privacy. In: RecSys 2019 (2019)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML 2017 (2017)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: AISTATS 2011 (2011)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retrieval 4(2), 133–151 (2001)
Guo, G., Zhang, J., Thalmann, D., Yorke-Smith, N.: ETAF: an extended trust antecedents framework for trust prediction. In: ASONAM 2014 (2014)
Ha, D., Dai, A., Le, Q.V.: Hypernetworks. In: ICLR 2016 (2016)
Hahnloser, R.H., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., Seung, H.S.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789), 947–951 (2000)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5(4), 1–19 (2015)
Hu, L., Sun, A., Liu, Y.: Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. In: SIGIR 2014 (2014)
Jiang, Y., Konečnỳ, J., Rush, K., Kannan, S.: Improving federated learning personalization via model agnostic meta learning. In: International Workshop on Federated Learning for User Privacy and Data Confidentiality in conjunction with NeurIPS 2019 (2019)
Lin, Y., et al.: Meta matrix factorization for federated rating predictions. In: SIGIR 2020 (2020)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
Müllner, P., Kowald, D., Lex, E.: User Groups for Robustness of Meta Matrix Factorization Against Decreasing Privacy Budgets (2020). https://doi.org/10.5281/zenodo.4031011
Schedl, M., Bauer, C.: Distance-and rank-based music mainstreaminess measurement. In: UMAP 2017 (2017)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: NIPS 2017 (2017)
Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30(1), 79–82 (2005)
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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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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