Federated Multi-view Matrix Factorization for Personalized Recommendations

• Published in: arXiv.org
• Main Authors: Flanagan, Adrian, Were Oyomno, Grigorievskiy, Alexander, Tan, Kuan Eeik, Khan, Suleiman A, Muhammad Ammad-Ud-Din
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 08.04.2020
• Subjects: Computer Science - Information Retrieval; Computer Science - Learning; Factorization; Statistics - Machine Learning
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2004.04256

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.