Deep Matrix Factorization Models for Recommender Systems

@inproceedings{Xue2017DeepMF,
  title={Deep Matrix Factorization Models for Recommender Systems},
  author={Hong Xue and Xinyu Dai and Jianbing Zhang and Shujian Huang and Jiajun Chen},
  booktitle={IJCAI},
  year={2017}
}
Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information. [] Key Method 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…

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