{"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/regularizing-matrix-factorization-with-user","title":"Regularizing Matrix Factorization with User and Item Embeddings for Recommendation","arxiv_id":"1809.00979","date":"2018-08-31","proceeding":null,"authors":["Thanh Tran","Kyumin Lee","Yiming Liao","Dongwon Lee"],"abstract":"Following recent successes in exploiting both latent factor and word\nembedding models in recommendation, we propose a novel Regularized\nMulti-Embedding (RME) based recommendation model that simultaneously\nencapsulates the following ideas via decomposition: (1) which items a user\nlikes, (2) which two users co-like the same items, (3) which two items users\noften co-liked, and (4) which two items users often co-disliked. In\nexperimental validation, the RME outperforms competing state-of-the-art models\nin both explicit and implicit feedback datasets, significantly improving\nRecall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition,\nunder the cold-start scenario for users with the lowest number of interactions,\nagainst the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in\nMovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source\ncode are available at: https://github.com/thanhdtran/RME.git.","url_abs":"http://arxiv.org/abs/1809.00979v1","url_pdf":"http://arxiv.org/pdf/1809.00979v1.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":"regularizing-matrix-factorization-with-user","repo_url":"https://github.com/thanhdtran/RME","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"regularizing-matrix-factorization-with-user","repo_url":"https://github.com/bdnf/SBX-Recommendation-Engine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.00979","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}