Deep Matrix Factorization Models for Recommender Systems

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…

• NN
  National Natural Science Foundation of China

  Awards: 61472183, 61672277
• NH
  National High-tech Research and Development Program

  Award: 2015AA015406