{"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/top-n-recommendation-with-novel-rank","title":"Top-N Recommendation with Novel Rank Approximation","arxiv_id":"1602.07783","date":"2016-02-25","proceeding":null,"authors":["Zhao Kang","Qiang Cheng"],"abstract":"The importance of accurate recommender systems has been widely recognized by\nacademia and industry. However, the recommendation quality is still rather low.\nRecently, a linear sparse and low-rank representation of the user-item matrix\nhas been applied to produce Top-N recommendations. This approach uses the\nnuclear norm as a convex relaxation for the rank function and has achieved\nbetter recommendation accuracy than the state-of-the-art methods. In the past\nseveral years, solving rank minimization problems by leveraging nonconvex\nrelaxations has received increasing attention. Some empirical results\ndemonstrate that it can provide a better approximation to original problems\nthan convex relaxation. In this paper, we propose a novel rank approximation to\nenhance the performance of Top-N recommendation systems, where the\napproximation error is controllable. Experimental results on real data show\nthat the proposed rank approximation improves the Top-$N$ recommendation\naccuracy substantially.","url_abs":"http://arxiv.org/abs/1602.07783v2","url_pdf":"http://arxiv.org/pdf/1602.07783v2.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":"top-n-recommendation-with-novel-rank","repo_url":"https://github.com/sckangz/SDM16","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}