JoTPaRS: A Job Title Prediction and Recommendation System for IT Professionals

Abstract

In today’s fast-evolving IT landscape, job seekers and recruiters face increasing challenges in aligning candidate qualifications with dynamic job roles and salary expectations. Traditional recruitment systems often rely on manual review or keyword-based algorithms, which lack contextual understanding and do not provide optimal job recommendations. In this paper, we propose JoTPaRS, a Job Title Prediction and Recommendation System that uses machine learning and natural language processing to accurately predict relevant IT job titles from user-provided descriptions, skills, and experience levels. In addition to multi-label classification and salary range suggestion, JoTPaRS introduces a novel Game Theory-based optimization layer that selects the most suitable job title among multiple predictions by modeling the decision as a risk-sensitive payoff game. This approach provides users with an optimal ranking of job recommendation, enhancing decision support for both job seekers and recruiters. Using a custom data set of 8,000 IT job descriptions, JoTPaRS achieved a high prediction accuracy of 95 63%, outperforming traditional machine learning models and recent benchmarks based on deep learning. User studies further demonstrate increased satisfaction with the system recommendations after the inclusion of the Game Theory-based refinement. The research suggests that predictive analytics with decision modeling can offer a more precise and personalized job matching experience in the digital hiring landscape.