{"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/collaborative-deep-learning-for-recommender","title":"Collaborative Deep Learning for Recommender Systems","arxiv_id":"1409.2944","date":"2014-09-10","proceeding":null,"authors":["Hao Wang","Naiyan Wang","Dit-yan Yeung"],"abstract":"Collaborative filtering (CF) is a successful approach commonly used by many\nrecommender systems. Conventional CF-based methods use the ratings given to\nitems by users as the sole source of information for learning to make\nrecommendation. However, the ratings are often very sparse in many\napplications, causing CF-based methods to degrade significantly in their\nrecommendation performance. To address this sparsity problem, auxiliary\ninformation such as item content information may be utilized. Collaborative\ntopic regression (CTR) is an appealing recent method taking this approach which\ntightly couples the two components that learn from two different sources of\ninformation. Nevertheless, the latent representation learned by CTR may not be\nvery effective when the auxiliary information is very sparse. To address this\nproblem, we generalize recent advances in deep learning from i.i.d. input to\nnon-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian\nmodel called collaborative deep learning (CDL), which jointly performs deep\nrepresentation learning for the content information and collaborative filtering\nfor the ratings (feedback) matrix. Extensive experiments on three real-world\ndatasets from different domains show that CDL can significantly advance the\nstate of the art.","url_abs":"http://arxiv.org/abs/1409.2944v2","url_pdf":"http://arxiv.org/pdf/1409.2944v2.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":"collaborative-deep-learning-for-recommender","repo_url":"https://github.com/domainxz/top-k-rec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1409.2944","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}