{"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/a-batch-learning-framework-for-scalable","title":"A Batch Learning Framework for Scalable Personalized Ranking","arxiv_id":"1711.04019","date":"2017-11-10","proceeding":null,"authors":["Kuan Liu","Prem Natarajan"],"abstract":"In designing personalized ranking algorithms, it is desirable to encourage a\nhigh precision at the top of the ranked list. Existing methods either seek a\nsmooth convex surrogate for a non-smooth ranking metric or directly modify\nupdating procedures to encourage top accuracy. In this work we point out that\nthese methods do not scale well to a large-scale setting, and this is partly\ndue to the inaccurate pointwise or pairwise rank estimation. We propose a new\nframework for personalized ranking. It uses batch-based rank estimators and\nsmooth rank-sensitive loss functions. This new batch learning framework leads\nto more stable and accurate rank approximations compared to previous work.\nMoreover, it enables explicit use of parallel computation to speed up training.\nWe conduct empirical evaluation on three item recommendation tasks. Our method\nshows consistent accuracy improvements over state-of-the-art methods.\nAdditionally, we observe time efficiency advantages when data scale increases.","url_abs":"http://arxiv.org/abs/1711.04019v1","url_pdf":"http://arxiv.org/pdf/1711.04019v1.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":"a-batch-learning-framework-for-scalable","repo_url":"https://github.com/skywaLKer518/A-Recsys","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}