{"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/sql-rank-a-listwise-approach-to-collaborative","title":"SQL-Rank: A Listwise Approach to Collaborative Ranking","arxiv_id":"1803.00114","date":"2018-02-28","proceeding":"ICML 2018 7","authors":["Liwei Wu","Cho-Jui Hsieh","James Sharpnack"],"abstract":"In this paper, we propose a listwise approach for constructing user-specific\nrankings in recommendation systems in a collaborative fashion. We contrast the\nlistwise approach to previous pointwise and pairwise approaches, which are\nbased on treating either each rating or each pairwise comparison as an\nindependent instance respectively. By extending the work of (Cao et al. 2007),\nwe cast listwise collaborative ranking as maximum likelihood under a\npermutation model which applies probability mass to permutations based on a low\nrank latent score matrix. We present a novel algorithm called SQL-Rank, which\ncan accommodate ties and missing data and can run in linear time. We develop a\ntheoretical framework for analyzing listwise ranking methods based on a novel\nrepresentation theory for the permutation model. Applying this framework to\ncollaborative ranking, we derive asymptotic statistical rates as the number of\nusers and items grow together. We conclude by demonstrating that our SQL-Rank\nmethod often outperforms current state-of-the-art algorithms for implicit\nfeedback such as Weighted-MF and BPR and achieve favorable results when\ncompared to explicit feedback algorithms such as matrix factorization and\ncollaborative ranking.","url_abs":"http://arxiv.org/abs/1803.00114v3","url_pdf":"http://arxiv.org/pdf/1803.00114v3.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":"sql-rank-a-listwise-approach-to-collaborative","repo_url":"https://github.com/wuliwei9278/SQL-Rank","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"collaborative-ranking","task_name":"Collaborative Ranking"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.00114","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}