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

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

24 Dec 2019arXiv:1912.11160archive 2025-07-28

Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey I. Nikolenko

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 β hyperparameter for the β-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.

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Tasks

Collaborative FilteringRecommendation Systems

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Recommendation Systems Million Song Dataset RecVAE Recall@20 0.276 #3 of 7 Archive leaderboard report
Recommendation Systems Million Song Dataset RecVAE Recall@50 0.374 #3 of 7 Archive leaderboard report
Recommendation Systems Million Song Dataset RecVAE nDCG@100 0.326 #3 of 7 Archive leaderboard report
Recommendation Systems MovieLens 20M RecVAE Recall@20 0.414 #7 of 18 Archive leaderboard report
Recommendation Systems MovieLens 20M RecVAE Recall@50 0.553 #7 of 18 Archive leaderboard report
Recommendation Systems MovieLens 20M RecVAE nDCG@100 0.442 #7 of 18 Archive leaderboard report
Recommendation Systems Netflix RecVAE Recall@20 0.361 #2 of 10 Archive leaderboard report
Recommendation Systems Netflix RecVAE Recall@50 0.452 #2 of 10 Archive leaderboard report
Recommendation Systems Netflix RecVAE nDCG@100 0.394 #2 of 10 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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