{"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-on-graphs","title":"Top-N Recommendation on Graphs","arxiv_id":"1609.08264","date":"2016-09-27","proceeding":null,"authors":["Zhao Kang","Chong Peng","Ming Yang","Qiang Cheng"],"abstract":"Recommender systems play an increasingly important role in online\napplications to help users find what they need or prefer. Collaborative\nfiltering algorithms that generate predictions by analyzing the user-item\nrating matrix perform poorly when the matrix is sparse. To alleviate this\nproblem, this paper proposes a simple recommendation algorithm that fully\nexploits the similarity information among users and items and intrinsic\nstructural information of the user-item matrix. The proposed method constructs\na new representation which preserves affinity and structure information in the\nuser-item rating matrix and then performs recommendation task. To capture\nproximity information about users and items, two graphs are constructed.\nManifold learning idea is used to constrain the new representation to be smooth\non these graphs, so as to enforce users and item proximities. Our model is\nformulated as a convex optimization problem, for which we need to solve the\nwell-known Sylvester equation only. We carry out extensive empirical\nevaluations on six benchmark datasets to show the effectiveness of this\napproach.","url_abs":"http://arxiv.org/abs/1609.08264v1","url_pdf":"http://arxiv.org/pdf/1609.08264v1.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-on-graphs","repo_url":"https://github.com/sckangz/CIKM16","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}