AI-Driven Intelligent Resume Recommendation Engine
DOI:
https://doi.org/10.32628/IJSRSET2512145Keywords:
Recommender System, Recruitment Process, Large Language Models (LLMs), Resume Recommendation, Feedback Mechanism, Applicant Tracking System (ATS)Abstract
Currently, internet recruitment platforms like Monster and Indeed.com have emerged as primary avenues for job seekers. These online platforms have offered their services for over a decade, significantly conserving time and resources for both job searchers and enterprises seeking to employ individuals. Nonetheless, conventional information retrieval methods may be unsuitable for consumers. The rationale is that the volume of results shown to a job seeker might be substantial, necessitating considerable time for them to study and evaluate their choices. The project seeks to create a system that can recommend the most appropriate resumes according to the job specifications submitted by recruiters via uploaded documents. The suggested system employs the BERT model to improve the accuracy and pertinence of job suggestions, ensuring a coherent alignment between abilities and work needs. Moreover, the method provides positive feedback to applicants whose qualifications may not align with the defined job criteria, beyond the conventional job suggestion procedure. This feedback method provides essential insights for personal and professional development while fostering honest and productive interactions between applicants and the recruiting process. Our technology not only offers immediate resume advice and comments but also predicts forthcoming employment prospects based on the candidate's skill set. This predictive function enables candidates to carefully plan their career trajectories and remain ahead of changing market expectations. This platform offers a revolutionary way to enhance job seeking and recruiting, guaranteeing an efficient, engaging, and simplified experience for all participants.
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