{"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-collaborative-filtering-with","title":"Hybrid Collaborative Filtering with Autoencoders","arxiv_id":"1603.00806","date":"2016-03-02","proceeding":null,"authors":["Florian Strub","Jeremie Mary","Romaric Gaudel"],"abstract":"Collaborative Filtering aims at exploiting the feedback of users to provide\npersonalised recommendations. Such algorithms look for latent variables in a\nlarge sparse matrix of ratings. They can be enhanced by adding side information\nto tackle the well-known cold start problem. While Neu-ral Networks have\ntremendous success in image and speech recognition, they have received less\nattention in Collaborative Filtering. This is all the more surprising that\nNeural Networks are able to discover latent variables in large and\nheterogeneous datasets. In this paper, we introduce a Collaborative Filtering\nNeural network architecture aka CFN which computes a non-linear Matrix\nFactorization from sparse rating inputs and side information. We show\nexperimentally on the MovieLens and Douban dataset that CFN outper-forms the\nstate of the art and benefits from side information. We provide an\nimplementation of the algorithm as a reusable plugin for Torch, a popular\nNeural Network framework.","url_abs":"http://arxiv.org/abs/1603.00806v3","url_pdf":"http://arxiv.org/pdf/1603.00806v3.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-collaborative-filtering-with","repo_url":"https://github.com/fstrub95/Autoencoders_cf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"torch","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}