{"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/data-driven-hr-resume-analysis-based-on","title":"Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning","arxiv_id":"1606.05611","date":"2016-06-17","proceeding":null,"authors":["Tim Zimmermann","Leo Kotschenreuther","Karsten Schmidt"],"abstract":"Recruiters usually spend less than a minute looking at each r\\'esum\\'e when\ndeciding whether it's worth continuing the recruitment process with the\ncandidate. Recruiters focus on keywords, and it's almost impossible to\nguarantee a fair process of candidate selection. The main scope of this paper\nis to tackle this issue by introducing a data-driven approach that shows how to\nprocess r\\'esum\\'es automatically and give recruiters more time to only examine\npromising candidates. Furthermore, we show how to leverage Machine Learning and\nNatural Language Processing in order to extract all required information from\nthe r\\'esum\\'es. Once the information is extracted, a ranking score is\ncalculated. The score describes how well the candidates fit based on their\neducation, work experience and skills. Later this paper illustrates a prototype\napplication that shows how this novel approach can increase the productivity of\nrecruiters. The application enables them to filter and rank candidates based on\npredefined job descriptions. Guided by the ranking, recruiters can get deeper\ninsights from candidate profiles and validate why and how the application\nranked them. This application shows how to improve the hiring process by giving\nan unbiased hiring decision support.","url_abs":"http://arxiv.org/abs/1606.05611v2","url_pdf":"http://arxiv.org/pdf/1606.05611v2.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":"data-driven-hr-resume-analysis-based-on","repo_url":"https://github.com/paszin/paszin.github.io","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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.05611","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}