Deep Matrix Factorization Models for Recommender Systems

@inproceedings{Xue2017DeepMF,
  title={Deep Matrix Factorization Models for Recommender Systems},
  author={Hong-Jian 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. 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
Highly Cited
This paper has 37 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 25 extracted citations

Learning Discrete Matrix Factorization Models

IEEE Signal Processing Letters • 2018
View 14 Excerpts
Method Support
Highly Influenced

Is Simple Better? Revisiting Non-Linear Matrix Factorization for Learning Incomplete Ratings

2018 IEEE International Conference on Data Mining Workshops (ICDMW) • 2018
View 11 Excerpts
Highly Influenced

Bayesian dual neural networks for recommendation

Frontiers of Computer Science • 2018
View 3 Excerpts
Method Support

References

Publications referenced by this paper.
Showing 1-10 of 27 references

Neural Collaborative Filtering

WWW • 2017
View 12 Excerpts
Highly Influenced

et al

Yao Wu, Christopher DuBois
Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pages 153– 162. ACM, • 2016
View 11 Excerpts
Highly Influenced

Adam: A Method for Stochastic Optimization

ICLR 2015 • 2015
View 4 Excerpts
Highly Influenced

IEEE

Yehuda Koren, Robert Bell, Chris Volinsky. Matrix factorization techniques for reco Computer
42(8):30–37, • 2009
View 6 Excerpts
Highly Influenced

Collaborative filtering with stacked denoising autoencoders and sparse inputs

Florian Strub, Jeremie Mary
NIPS Workshop on Machine Learning for eCommerce, • 2015
View 1 Excerpt

In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Sheng Li, Jaya Kawale, Yun Fu. Deep collaborative filtering via marginalized autoencoder
pages 811–820. ACM, • 2015
View 1 Excerpt

In Proceedings of the 24th International Conference on World Wide Web

Ali Mamdouh Elkahky, Yang Song, Xiaodong He. A multi-view deep learning approach for cross systems
pages 278–288. ACM, • 2015
View 2 Excerpts