RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback

Code

• github.com/ilya-shenbin/RecVAE
  OfficialIn paperpytorch★ 0
• github.com/SharonLSY/CDRC-MSc-Recommender-Systems
  none★ 1
• github.com/SharonLSY/CDRC-MSc---Recommender-Systems
  none★ 1

Abstract

Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering. In particular, the recently proposed Mult-VAE model, which used the multinomial likelihood variational autoencoders, has shown excellent results for top-N recommendations. In this work, we propose the Recommender VAE (RecVAE) model that originates from our research on regularization techniques for variational autoencoders. RecVAE introduces several novel ideas to improve Mult-VAE, including a novel composite prior distribution for the latent codes, a new approach to setting the hyperparameter for the -VAE framework, and a new approach to training based on alternating updates. In experimental evaluation, we show that RecVAE significantly outperforms previously proposed autoencoder-based models, including Mult-VAE and RaCT, across classical collaborative filtering datasets, and present a detailed ablation study to assess our new developments. Code and models are available at https://github.com/ilya-shenbin/RecVAE.

Tasks

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Reproductions