AI-Based Resume Screening and Candidate Ranking Using Hybrid Machine Learning Techniques

This study is about using Artificial Intelligence to help with screening resumes and ranking candidates. The people who did the study found out that using Natural Language Processing with machine learning algorithms makes the recruitment process more efficient, accurate and reliable. The old way of screening resumes takes a lot of time. Can be unfair but the new system is automated and objective. The study shows that things like skill match, experience, education, certifications and Artificial Intelligence skills are very important when deciding if a candidate is suitable. Skill match and Artificial Intelligence skills are the important because companies need people who are good with technology. The hybrid machine learning model, the Random Forest algorithm was very accurate and good at handling complex recruitment data. The new system helps companies move from hiring to automated hiring. Recruiters can use data to find the candidates save time and make better hiring decisions. The system is also fair and transparent because it reduces bias and uses the same criteria to evaluate everyone. In conclusion using Artificial Intelligence, Natural Language Processing and hybrid machine learning techniques is a way to solve modern recruitment problems. The study helps the field of Artificial Intelligence in Human Resources by presenting a framework, for automated resume screening and candidate ranking. Companies should use these kinds of systems to make their recruitment process better find the talent and stay ahead in the digital age.

1.                Bassi, L. (2011). Raging debates in HR analytics. People and Strategy, 34(2), 14–18. 2.                Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, 88(10), 52–58. 3.                Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699 4.                Minbaeva, D. (2018). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701–713. https://doi.org/10.1002/hrm.21848 5.                Levenson, A. (2018). Using workforce analytics to improve strategy execution. Human Resource Management, 57(3), 685–700. https://doi.org/10.1002/hrm.21848 6.                Strohmeier, S., & Piazza, F. (2015). Artificial intelligence techniques in human resource management. German Journal of Human Resource Management, 29(3–4), 262–281. https://doi.org/10.1177/2397002215590049 7.                Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11. https://doi.org/10.1111/1748-8583.12090 8.                Fitz-enz, J. (2010). The new HR analytics: Predicting the economic value of your company’s human capital investments. AMACOM. 9.                Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236–242. https://doi.org/10.1016/j.orgdyn.2015.05.008 10.             Van den Heuvel, S., & Bondarouk, T. (2017). The rise (and fall?) of HR analytics: A study into the future application, value, structure, and system support. The International Journal of Human Resource Management, 28(1), 1–24.