{"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/signed-distance-based-deep-memory-recommender","title":"Signed Distance-based Deep Memory Recommender","arxiv_id":"1905.00453","date":"2019-05-01","proceeding":null,"authors":["Thanh Tran","Xinyue Liu","Kyumin Lee","Xiangnan Kong"],"abstract":"Personalized recommendation algorithms learn a user's preference for an item\nby measuring a distance/similarity between them. However, some of the existing\nrecommendation models (e.g., matrix factorization) assume a linear relationship\nbetween the user and item. This approach limits the capacity of recommender\nsystems, since the interactions between users and items in real-world\napplications are much more complex than the linear relationship. To overcome\nthis limitation, in this paper, we design and propose a deep learning framework\ncalled Signed Distance-based Deep Memory Recommender, which captures non-linear\nrelationships between users and items explicitly and implicitly, and work well\nin both general recommendation task and shopping basket-based recommendation\ntask. Through an extensive empirical study on six real-world datasets in the\ntwo recommendation tasks, our proposed approach achieved significant\nimprovement over ten state-of-the-art recommendation models.","url_abs":"http://arxiv.org/abs/1905.00453v1","url_pdf":"http://arxiv.org/pdf/1905.00453v1.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":"signed-distance-based-deep-memory-recommender","repo_url":"https://github.com/thanhdtran/SDMR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"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}