{"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/solving-the-apparent-diversity-accuracy","title":"Solving the apparent diversity-accuracy dilemma of recommender systems","arxiv_id":null,"date":"2010-03-09","proceeding":"PNAS ∣ March 9 ∣ vol. 107 ∣ no. 10 ∣ 4511–4515 2010 3","authors":["Tao Zhoua","Zoltán Kuscsika","Jian-Guo Liua","Matúš Medoa","Joseph Rushton Wakelinga","Yi-Cheng Zhanga"],"abstract":"Recommender systems use data on past user preferences to predict\r\npossible future likes and interests. A key challenge is that while the\r\nmost useful individual recommendations are to be found among\r\ndiverse niche objects, the most reliably accurate results are ob-\r\ntained by methods that recommend objects based on user or object\r\nsimilarity. In this paper we introduce a new algorithm specifically to\r\naddress the challenge of diversity and show how it can be used to\r\nresolve this apparent dilemma when combined in an elegant\r\nhybrid with an accuracy-focused algorithm. By tuning the hybrid\r\nappropriately we are able to obtain, without relying on any seman-\r\ntic or context-specific information, simultaneous gains in both\r\naccuracy and diversity of recommendations.","url_abs":"https://www.pnas.org/doi/pdf/10.1073/pnas.1000488107","url_pdf":"https://www.pnas.org/doi/pdf/10.1073/pnas.1000488107","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":"solving-the-apparent-diversity-accuracy","repo_url":"https://github.com/rodfloripa/Heats_Probs_Recommender","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"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}