Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the basic idea to predict a personalized ranking over a set of items for an individual user with the similarities among users and items. In this paper, we propose a novel matrix factorization model with neural network architecture. Firstly, we construct a user-item matrix with explicit ratings and non-preference implicit feedback. With this matrix as the input, we present a deep structure learning architecture to learn a common low dimensional space for the representations of users and items. Secondly, we design a new loss function based on binary cross entropy, in which we consider both explicit ratings and implicit feedback for a better optimization. The experimental results show the effectiveness of both our proposed model and the loss function. On several benchmark datasets, our model outperformed other stateof-the-art methods. We also conduct extensive experiments to evaluate the performance within different experimental settings.
DMF REF is a deep matrix factorization model which uses multiple nonlinear layers to process both explicit ratings and implicit feedback of users and news.
Deep matrix factorization models for recommender systems
IJCAI
Computer Science