Federated Multi-View Matrix Factorization for Personalized Recommendations
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.
Cite
Text
Flanagan et al. "Federated Multi-View Matrix Factorization for Personalized Recommendations." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67661-2_20Markdown
[Flanagan et al. "Federated Multi-View Matrix Factorization for Personalized Recommendations." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/flanagan2020ecmlpkdd-federated/) doi:10.1007/978-3-030-67661-2_20BibTeX
@inproceedings{flanagan2020ecmlpkdd-federated,
title = {{Federated Multi-View Matrix Factorization for Personalized Recommendations}},
author = {Flanagan, Adrian and Oyomno, Were and Grigorievskiy, Alexander and Tan, Kuan Eeik and Khan, Suleiman A. and Ammad-ud-din, Muhammad},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2020},
pages = {324-347},
doi = {10.1007/978-3-030-67661-2_20},
url = {https://mlanthology.org/ecmlpkdd/2020/flanagan2020ecmlpkdd-federated/}
}