{"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/deep-factorization-machines-for-knowledge","title":"Deep Factorization Machines for Knowledge Tracing","arxiv_id":"1805.00356","date":"2018-05-01","proceeding":null,"authors":["Jill-Jênn Vie"],"abstract":"This paper introduces our solution to the 2018 Duolingo Shared Task on Second\nLanguage Acquisition Modeling (SLAM). We used deep factorization machines, a\nwide and deep learning model of pairwise relationships between users, items,\nskills, and other entities considered. Our solution (AUC 0.815) hopefully\nmanaged to beat the logistic regression baseline (AUC 0.774) but not the top\nperforming model (AUC 0.861) and reveals interesting strategies to build upon\nitem response theory models.","url_abs":"http://arxiv.org/abs/1805.00356v1","url_pdf":"http://arxiv.org/pdf/1805.00356v1.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":"deep-factorization-machines-for-knowledge","repo_url":"https://github.com/jilljenn/ktm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"knowledge-tracing","task_name":"Knowledge Tracing"},{"task_slug":"language-acquisition","task_name":"Language Acquisition"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}