Related papers: Federated Multi-view Matrix Factorization for Personalized Recommendations

Federated Multi-view Matrix Factorization for Personalized Recommendations

URL: http://arxiv.org/abs/2004.04256v1
Date: Wed, 8 Apr 2020 21:07:50 GMT
Title: Federated Multi-view Matrix Factorization for Personalized Recommendations
Authors: Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A. Khan, and Muhammad Ammad-Ud-Din
Abstract summary: 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.
Score: 53.74747022749739
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
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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