Deep Latent Factor Model for Collaborative Filtering

Aanchal Mongia; Neha Jhamb; Emilie Chouzenoux; Angshul Majumdar

Deep Latent Factor Model for Collaborative Filtering

Machine Learning 2019-12-11 v1 Information Retrieval Machine Learning

Authors: Aanchal Mongia , Neha Jhamb , Emilie Chouzenoux , Angshul Majumdar

Abstract

Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all state-of-the-art collaborative filtering techniques.

Keywords

nonnegative matrix factorization recommendation system machine learning

Cite

@article{arxiv.1912.04754,
  title  = {Deep Latent Factor Model for Collaborative Filtering},
  author = {Aanchal Mongia and Neha Jhamb and Emilie Chouzenoux and Angshul Majumdar},
  journal= {arXiv preprint arXiv:1912.04754},
  year   = {2019}
}

Comments

This is an initial draft of the accepted paper at Elsevier Signal Processing

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