Deep Matrix Factorization Models for Recommender Systems

  title={Deep Matrix Factorization Models for Recommender Systems},
  author={Hong-Jian Xue and Xinyu Dai and Jianbing Zhang and Shujian Huang and Jiajun Chen},
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… CONTINUE READING
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