{"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/npe-neural-personalized-embedding-for","title":"NPE: Neural Personalized Embedding for Collaborative Filtering","arxiv_id":"1805.06563","date":"2018-05-17","proceeding":null,"authors":["ThaiBinh Nguyen","Atsuhiro Takasu"],"abstract":"Matrix factorization is one of the most efficient approaches in recommender\nsystems. However, such algorithms, which rely on the interactions between users\nand items, perform poorly for \"cold-users\" (users with little history of such\ninteractions) and at capturing the relationships between closely related items.\nTo address these problems, we propose a neural personalized embedding (NPE)\nmodel, which improves the recommendation performance for cold-users and can\nlearn effective representations of items. It models a user's click to an item\nin two terms: the personal preference of the user for the item, and the\nrelationships between this item and other items clicked by the user. We show\nthat NPE outperforms competing methods for top-N recommendations, specially for\ncold-user recommendations. We also performed a qualitative analysis that shows\nthe effectiveness of the representations learned by the model.","url_abs":"http://arxiv.org/abs/1805.06563v1","url_pdf":"http://arxiv.org/pdf/1805.06563v1.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":"npe-neural-personalized-embedding-for","repo_url":"https://github.com/zhrlove/NPE","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":"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}