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.
For example, in REF , a deep learning architecture was presented to learn a common low dimensional space for the representations of users and items, where a two-pathway neural network architecture was used to replace the linear embedding operation.
Deep matrix factorization models for recommender systems
IJCAI
Computer Science