Published Paper Details:

The goal of "AI-Driven Talent Matching: Empowering HR Professionals with Reinforcement Learning" is to transform hiring practices by fostering a mutually beneficial partnership between employers and employees. When HR specialists thoroughly specify Job Descriptions (JDs) on the platform, the project gets underway. Meanwhile, job applicants upload their resumes, resulting in the establishment of profiles summarizing their credentials and experiences. After that, the system gathers essential data to create unique JDs for each seeker. The degree to which these customized JDs resemble the HR's JD is measured by a computed similarity score, guaranteeing a data-driven method of candidate assessment. Based on these scores, candidates are ranked, and Recruiters are then presented with top ranked candidates by applying a threshold. The incorporation of reinforcement learning will improve the recommendation model through learning from recruiter's feedback. Feedback in the form of "yes" or "no" from recruiter reviews of candidates enables the model to dynamically modify the similarity score threshold. This research explores the shifting dynamics of the labor market and makes the case for a datadriven strategy that would enable recruiters to make well-informed choices when hiring and selecting candidates. The process ends with a screening test to make sure applicants have the skills that are needed for the job. Better recruitment outcomes are promised by this cutting-edge approach, which will help both Recruiters and job seekers.