{"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/fastfm-a-library-for-factorization-machines","title":"fastFM: A Library for Factorization Machines","arxiv_id":"1505.00641","date":"2015-05-04","proceeding":null,"authors":["Immanuel Bayer"],"abstract":"Factorization Machines (FM) are only used in a narrow range of applications\nand are not part of the standard toolbox of machine learning models. This is a\npity, because even though FMs are recognized as being very successful for\nrecommender system type applications they are a general model to deal with\nsparse and high dimensional features. Our Factorization Machine implementation\nprovides easy access to many solvers and supports regression, classification\nand ranking tasks. Such an implementation simplifies the use of FM's for a wide\nfield of applications. This implementation has the potential to improve our\nunderstanding of the FM model and drive new development.","url_abs":"http://arxiv.org/abs/1505.00641v3","url_pdf":"http://arxiv.org/pdf/1505.00641v3.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":"fastfm-a-library-for-factorization-machines","repo_url":"https://github.com/ibayer/fastFM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}