Enhancing Resume Recommendation System through Skill-based Similarity using Deep Learning Models

This study proposes a novel methodology for resume recommendation systems, showcasing the efficacy of combining Word2Vec and LSTM-RNN models for skill similarity assessment and predicting job profiles. Specifically, this research aims to elucidate the advantages of deploying deep learning tools in resume analysis, both for recruiters seeking streamlined processes and candidates navigating the intricacies of modern hiring practices. In addition, this paper explores the potential for users to learn new skills based on the recommended jobs, contributing to a more dynamic and adaptive approach to career development and skill acquisition. Lastly, the implications of our research are discussed, shedding light on how our findings contribute to the broader understanding of resume recommendations systems and their role in optimizing the recruitment process.