{"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/a-hybrid-variational-autoencoder-for","title":"A Hybrid Variational Autoencoder for Collaborative Filtering","arxiv_id":"1808.01006","date":"2018-07-14","proceeding":null,"authors":["Kilol Gupta","Mukund Yelahanka Raghuprasad","Pankhuri Kumar"],"abstract":"In today's day and age when almost every industry has an online presence with\nusers interacting in online marketplaces, personalized recommendations have\nbecome quite important. Traditionally, the problem of collaborative filtering\nhas been tackled using Matrix Factorization which is linear in nature. We\nextend the work of [11] on using variational autoencoders (VAEs) for\ncollaborative filtering with implicit feedback by proposing a hybrid,\nmulti-modal approach. Our approach combines movie embeddings (learned from a\nsibling VAE network) with user ratings from the Movielens 20M dataset and\napplies it to the task of movie recommendation. We empirically show how the VAE\nnetwork is empowered by incorporating movie embeddings. We also visualize movie\nand user embeddings by clustering their latent representations obtained from a\nVAE.","url_abs":"http://arxiv.org/abs/1808.01006v2","url_pdf":"http://arxiv.org/pdf/1808.01006v2.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":"a-hybrid-variational-autoencoder-for","repo_url":"https://github.com/kilolgupta/Variational-Autoencoders-Collaborative-Filtering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-hybrid-variational-autoencoder-for","repo_url":"https://github.com/nathann3/better_than_netflix_movie_recommender","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"a-hybrid-variational-autoencoder-for","repo_url":"https://github.com/microsoft/recommenders","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"movie-recommendation","task_name":"Movie Recommendation"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}