{"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/field-aware-factorization-machines-in-a-real","title":"Field-aware Factorization Machines in a Real-world Online Advertising System","arxiv_id":"1701.04099","date":"2017-01-15","proceeding":null,"authors":["Yuchin Juan","Damien Lefortier","Olivier Chapelle"],"abstract":"Predicting user response is one of the core machine learning tasks in\ncomputational advertising. Field-aware Factorization Machines (FFM) have\nrecently been established as a state-of-the-art method for that problem and in\nparticular won two Kaggle challenges. This paper presents some results from\nimplementing this method in a production system that predicts click-through and\nconversion rates for display advertising and shows that this method it is not\nonly effective to win challenges but is also valuable in a real-world\nprediction system. We also discuss some specific challenges and solutions to\nreduce the training time, namely the use of an innovative seeding algorithm and\na distributed learning mechanism.","url_abs":"http://arxiv.org/abs/1701.04099v3","url_pdf":"http://arxiv.org/pdf/1701.04099v3.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":"field-aware-factorization-machines-in-a-real","repo_url":"https://github.com/cpapadimitriou/Click-Through-Rate-prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"field-aware-factorization-machines-in-a-real","repo_url":"https://github.com/guestwalk/libffm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"field-aware-factorization-machines-in-a-real","repo_url":"https://github.com/ycjuan/libffm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"field-aware-factorization-machines-in-a-real","repo_url":"https://github.com/xue-pai/FuxiCTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1701.04099","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}