Artificial Intelligence-Based Modeling for Recruiting Young Professionals

Abstract

The development of social networks, the popularization of remote work, and globalization provide a basis for the transformation of HR management. The efficiency and quality of implementing existing recruitment technologies are enhanced through their digitalization: the use of integrated mobile applications and automation of HR processes, digital integration with cloud services, the use of predictive people analytics, augmented reality (AR), virtual reality (VR), and artificial intelligence (AI). However, there is an existing problem of job search for young professionals, which lies in the lack of experience, competition, and heterogeneity of educational programs. Because of this, employers find it challenging to evaluate and hire such candidates. Hiring young professionals is important for companies for various reasons, including preparing future specialists and accessing academic resources. The article investigates the potential of using intelligent-based methods for recruiting young professionals, which includes automating routine tasks, accelerating candidate selection, and evaluating the objectivity of candidates. The study employs machine-learning methods for predicting the hiring of young professionals: logistic regression, linear regression, decision trees, random forest, and XGBoost. The R programming language was used for data analysis; the Python programming language and the SciKit-Learn and XGBoost libraries were used for training and testing machine-learning models. The models take into account the academic performance of young professionals and their salary levels. The study's results can be used to optimize the recruitment process for enterprises and organizations. Artificial intelligence can assist in automating candidate selection, analyzing their skills, and better matching job requirements. The research can help young professionals better understand labor market requirements, gain valuable advice and insights on how to prepare and increase their competitiveness in the job search.