论文标题
个性化建议的联合多视图矩阵分解
Federated Multi-view Matrix Factorization for Personalized Recommendations
论文作者
论文摘要
我们介绍了联合多视图矩阵分解方法，该方法将联合学习框架扩展到具有多个数据源的矩阵分解。我们的方法能够学习多视图模型，而无需将用户的个人数据传输到中央服务器。据我们所知，这是使用多视图矩阵分解提供建议的第一个联合模型。该模型在生产设置的三个数据集上进行了严格评估。经验验证证实，联合的多视图矩阵分解优于不考虑数据的多视图结构的更简单方法，此外，它证明了所提出的方法在挑战性的预测任务中的有用性。
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.
