{"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/autorec-autoencoders-meet-collaborative","title":"AutoRec: Autoencoders Meet Collaborative Filtering","arxiv_id":null,"date":"2015-05-18","proceeding":"Proceedings of the 24th International Conference on World Wide Web 2015 5","authors":["Suvash Sedhain","Aditya Krishna Menon","Scott Sanner","Lexing Xie"],"abstract":"This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec’s compact and efficiently trainable model outperforms stateof-the-art CF techniques (biased matrix factorization, RBMCF and LLORMA) on the Movielens and Netflix datasets.","url_abs":"https://scholar.google.com/citations?user=z_hDjNYAAAAJ&hl=en&oi=sra","url_pdf":"http://users.cecs.anu.edu.au/~u5098633/papers/www15.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":"autorec-autoencoders-meet-collaborative","repo_url":"https://github.com/gtshs2/Autorec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"autorec-autoencoders-meet-collaborative","repo_url":"https://github.com/tuanio/AutoRec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"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-movielens-10m","task":"Recommendation Systems","dataset":"MovieLens 10M","model":"I-AutoRec","rank_in_archive_order":10,"of":17,"metrics":{"RMSE":"0.782"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-1m","task":"Recommendation Systems","dataset":"MovieLens 1M","model":"I-AutoRec","rank_in_archive_order":5,"of":31,"metrics":{"RMSE":"0.831"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}