{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/recvae-a-new-variational-autoencoder-for-top","title":"RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback","arxiv_id":"1912.11160","date":"2019-12-24","proceeding":null,"authors":["Ilya Shenbin","Anton Alekseev","Elena Tutubalina","Valentin Malykh","Sergey I. Nikolenko"],"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 $\\beta$ hyperparameter for the $\\beta$-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.","url_abs":"https://arxiv.org/abs/1912.11160v1","url_pdf":"https://arxiv.org/pdf/1912.11160v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"recvae-a-new-variational-autoencoder-for-top","repo_url":"https://github.com/ilya-shenbin/RecVAE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"recvae-a-new-variational-autoencoder-for-top","repo_url":"https://github.com/SharonLSY/CDRC-MSc---Recommender-Systems","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"recvae-a-new-variational-autoencoder-for-top","repo_url":"https://github.com/SharonLSY/CDRC-MSc-Recommender-Systems","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/collaborative-filtering-on-million-song","task":"Recommendation Systems","dataset":"Million Song Dataset","model":"RecVAE","rank_in_archive_order":3,"of":7,"metrics":{"Recall@20":"0.276","Recall@50":"0.374","nDCG@100":"0.326"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-20m","task":"Recommendation Systems","dataset":"MovieLens 20M","model":"RecVAE","rank_in_archive_order":7,"of":18,"metrics":{"Recall@20":"0.414","Recall@50":"0.553","nDCG@100":"0.442"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-netflix","task":"Recommendation Systems","dataset":"Netflix","model":"RecVAE","rank_in_archive_order":2,"of":10,"metrics":{"Recall@20":"0.361","Recall@50":"0.452","nDCG@100":"0.394"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1912.11160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.11160"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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