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.

提出了一种使用联邦学习框架的多视图矩阵分解方法，旨在学习多视图模型且无需将用户的个人数据传输到中央服务器。在三个生产数据集上进行了实证验证，结果表明这种方法在联邦推荐任务的冷启动预测方面表现优于不考虑数据多视图结构的简单方法，同时证明了该方法对复杂的预测任务的有用性。