{"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/hybrid-recommender-system-based-on","title":"Hybrid Recommender System based on Autoencoders","arxiv_id":"1606.07659","date":"2016-06-24","proceeding":null,"authors":["Florian Strub","Romaric Gaudel","Jérémie Mary"],"abstract":"A standard model for Recommender Systems is the Matrix Completion setting:\ngiven partially known matrix of ratings given by users (rows) to items\n(columns), infer the unknown ratings. In the last decades, few attempts where\ndone to handle that objective with Neural Networks, but recently an\narchitecture based on Autoencoders proved to be a promising approach. In\ncurrent paper, we enhanced that architecture (i) by using a loss function\nadapted to input data with missing values, and (ii) by incorporating side\ninformation. The experiments demonstrate that while side information only\nslightly improve the test error averaged on all users/items, it has more impact\non cold users/items.","url_abs":"http://arxiv.org/abs/1606.07659v3","url_pdf":"http://arxiv.org/pdf/1606.07659v3.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":"hybrid-recommender-system-based-on","repo_url":"https://github.com/fstrub95/Autoencoders_cf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":null},{"paper_slug":"hybrid-recommender-system-based-on","repo_url":"https://github.com/Recvani/benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"hybrid-recommender-system-based-on","repo_url":"https://github.com/SJD1882/Big-Data-Recommender-Systems","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"hybrid-recommender-system-based-on","repo_url":"https://github.com/jowoojun/collaborative_filtering_keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"matrix-completion","task_name":"Matrix Completion"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/collaborative-filtering-on-douban","task":"Recommendation Systems","dataset":"Douban","model":"I-CFN","rank_in_archive_order":1,"of":7,"metrics":{"RMSE":"0.6911"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-douban","task":"Recommendation Systems","dataset":"Douban","model":"U-CFN","rank_in_archive_order":2,"of":7,"metrics":{"RMSE":"0.7049"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-10m","task":"Recommendation Systems","dataset":"MovieLens 10M","model":"I-CFN","rank_in_archive_order":8,"of":17,"metrics":{"RMSE":"0.7767"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-10m","task":"Recommendation Systems","dataset":"MovieLens 10M","model":"U-CFN","rank_in_archive_order":12,"of":17,"metrics":{"RMSE":"0.7954"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-1m","task":"Recommendation Systems","dataset":"MovieLens 1M","model":"I-CFN","rank_in_archive_order":7,"of":31,"metrics":{"RMSE":"0.8321"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-1m","task":"Recommendation Systems","dataset":"MovieLens 1M","model":"U-CFN","rank_in_archive_order":14,"of":31,"metrics":{"RMSE":"0.8574"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}