{"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/deepcf-a-unified-framework-of-representation","title":"DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System","arxiv_id":"1901.04704","date":"2019-01-15","proceeding":null,"authors":["Zhi-Hong Deng","Ling Huang","Chang-Dong Wang","Jian-Huang Lai","Philip S. Yu"],"abstract":"In general, recommendation can be viewed as a matching problem, i.e., match\nproper items for proper users. However, due to the huge semantic gap between\nusers and items, it's almost impossible to directly match users and items in\ntheir initial representation spaces. To solve this problem, many methods have\nbeen studied, which can be generally categorized into two types, i.e.,\nrepresentation learning-based CF methods and matching function learning-based\nCF methods. Representation learning-based CF methods try to map users and items\ninto a common representation space. In this case, the higher similarity between\na user and an item in that space implies they match better. Matching function\nlearning-based CF methods try to directly learn the complex matching function\nthat maps user-item pairs to matching scores. Although both methods are well\ndeveloped, they suffer from two fundamental flaws, i.e., the limited\nexpressiveness of dot product and the weakness in capturing low-rank relations\nrespectively. To this end, we propose a general framework named DeepCF, short\nfor Deep Collaborative Filtering, to combine the strengths of the two types of\nmethods and overcome such flaws. Extensive experiments on four publicly\navailable datasets demonstrate the effectiveness of the proposed DeepCF\nframework.","url_abs":"http://arxiv.org/abs/1901.04704v1","url_pdf":"http://arxiv.org/pdf/1901.04704v1.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":"deepcf-a-unified-framework-of-representation","repo_url":"https://github.com/familyld/DeepCF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deepcf-a-unified-framework-of-representation","repo_url":"https://github.com/rowantseng/DeepCF-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}