{"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/learning-compatibility-across-categories-for","title":"Learning Compatibility Across Categories for Heterogeneous Item Recommendation","arxiv_id":"1603.09473","date":"2016-03-31","proceeding":null,"authors":["Ruining He","Charles Packer","Julian McAuley"],"abstract":"Identifying relationships between items is a key task of an online\nrecommender system, in order to help users discover items that are functionally\ncomplementary or visually compatible. In domains like clothing recommendation,\nthis task is particularly challenging since a successful system should be\ncapable of handling a large corpus of items, a huge amount of relationships\namong them, as well as the high-dimensional and semantically complicated\nfeatures involved. Furthermore, the human notion of \"compatibility\" to capture\ngoes beyond mere similarity: For two items to be compatible---whether jeans and\na t-shirt, or a laptop and a charger---they should be similar in some ways, but\nsystematically different in others.\n  In this paper we propose a novel method, Monomer, to learn complicated and\nheterogeneous relationships between items in product recommendation settings.\nRecently, scalable methods have been developed that address this task by\nlearning similarity metrics on top of the content of the products involved.\nHere our method relaxes the metricity assumption inherent in previous work and\nmodels multiple localized notions of 'relatedness,' so as to uncover ways in\nwhich related items should be systematically similar, and systematically\ndifferent. Quantitatively, we show that our system achieves state-of-the-art\nperformance on large-scale compatibility prediction tasks, especially in cases\nwhere there is substantial heterogeneity between related items. Qualitatively,\nwe demonstrate that richer notions of compatibility can be learned that go\nbeyond similarity, and that our model can make effective recommendations of\nheterogeneous content.","url_abs":"http://arxiv.org/abs/1603.09473v3","url_pdf":"http://arxiv.org/pdf/1603.09473v3.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":"learning-compatibility-across-categories-for","repo_url":"https://github.com/appier/compatibility-family-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"product-recommendation","task_name":"Product Recommendation"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}