{"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/conformative-filtering-for-implicit-feedback","title":"Conformative Filtering for Implicit Feedback Data","arxiv_id":"1704.01889","date":"2017-04-06","proceeding":null,"authors":["Farhan Khawar","Nevin L. Zhang"],"abstract":"Implicit feedback is the simplest form of user feedback that can be used for\nitem recommendation. It is easy to collect and is domain independent. However,\nthere is a lack of negative examples. Previous work tackles this problem by\nassuming that users are not interested or not as much interested in the\nunconsumed items. Those assumptions are often severely violated since\nnon-consumption can be due to factors like unawareness or lack of resources.\nTherefore, non-consumption by a user does not always mean disinterest or\nirrelevance. In this paper, we propose a novel method called Conformative\nFiltering (CoF) to address the issue. The motivating observation is that if\nthere is a large group of users who share the same taste and none of them have\nconsumed an item before, then it is likely that the item is not of interest to\nthe group. We perform multidimensional clustering on implicit feedback data\nusing hierarchical latent tree analysis (HLTA) to identify user `tastes' groups\nand make recommendations for a user based on her memberships in the groups and\non the past behavior of the groups. Experiments on two real-world datasets from\ndifferent domains show that CoF has superior performance compared to several\ncommon baselines.","url_abs":"http://arxiv.org/abs/1704.01889v2","url_pdf":"http://arxiv.org/pdf/1704.01889v2.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":"conformative-filtering-for-implicit-feedback","repo_url":"https://github.com/fkhawar/Conformative-Filtering","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}