Key points are not available for this paper at this time.
The proposed work introduces an innovative recruitment approach integrating LinkedIn web scraping to compile a comprehensive dataset of job descriptions and essential skills. Utilizing a pioneering Word2Vec-driven machine learning model, it efficiently categorizes job descriptions, transforming hiring practices. Job seekers benefit from a dedicated platform offering concise resume summaries and a tailored algorithm for precise role matching. Automation and personalization converge to craft custom interview questions, bridging the recruiter-job seeker gap. The goal is to enhance hiring efficiency, optimize career opportunities and talent acquisition. Performance metrics validate the model's accuracy and effectiveness in job title predictions, ensuring informed decisions for both recruiters and job seekers.
Julian et al. (2024) studied this question.