Corpus ID: 221150449

Joint Variational Autoencoders for Recommendation with Implicit Feedback

@article{Askari2020JointVA,
  title={Joint Variational Autoencoders for Recommendation with Implicit Feedback},
  author={Bahare Askari and Jaroslaw Szlichta and Amirali Salehi-Abari},
  journal={ArXiv},
  year={2020},
  volume={abs/2008.07577}
}
Variational Autoencoders (VAEs) have recently shown promising performance in collaborative filtering with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. We introduce joint variational autoencoders (JoVA), an ensemble of two VAEs, in which VAEs jointly learn both user and item representations and collectively reconstruct and predict user preferences. This design allows JoVA to capture user-user and item-item… Expand
3 Citations
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