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arXiv · 2004.04256

Federated Multi-view Matrix Factorization for Personalized Recommendations

Adrian Flanagan·Were Oyomno·Alexander Grigorievskiy·Kuan Eeik Tan·Suleiman A. Khan·Muhammad Ammad-Ud-Din
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Abstract

We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources. Our method is able to learn the multi-view model without transferring the user's personal data to a central server. As far as we are aware this is the first federated model to provide recommendations using multi-view matrix factorization. The model is rigorously evaluated on three datasets on production settings. Empirical validation confirms that federated multi-view matrix factorization outperforms simpler methods that do not take into account the multi-view structure of the data, in addition, it demonstrates the usefulness of the proposed method for the challenging prediction tasks of cold-start federated recommendations.

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Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A. Khan, Muhammad Ammad-Ud-Din. 2020-04-08. Federated Multi-view Matrix Factorization for Personalized Recommendations. https://doi.org/10.1007/978-3-030-67661-2_20

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