{"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-for-ctr","title":"Field-aware factorization machines for CTR prediction","arxiv_id":null,"date":"2016-09-07","proceeding":"RecSys 2016 9","authors":["Yuchin Juan","Yong Zhuang","Wei-Sheng Chin","Chih-Jen Lin"],"abstract":"Click-through rate (CTR) prediction plays an important role\r\nin computational advertising. Models based on degree-2\r\npolynomial mappings and factorization machines (FMs) are\r\nwidely used for this task. Recently, a variant of FMs, fieldaware factorization machines (FFMs), outperforms existing\r\nmodels in some world-wide CTR-prediction competitions.\r\nBased on our experiences in winning two of them, in this\r\npaper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training\r\nFFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments\r\nshow that FFMs are very useful for certain classification\r\nproblems. Finally, we have released a package of FFMs for\r\npublic use.","url_abs":"https://dl.acm.org/doi/10.1145/2959100.2959134","url_pdf":"https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.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-for-ctr","repo_url":"https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/rank/ffm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null},{"paper_slug":"field-aware-factorization-machines-for-ctr","repo_url":"https://github.com/UlionTse/mlgb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"field-aware-factorization-machines-for-ctr","repo_url":"https://github.com/microsoft/recommenders","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"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}