ResuMatcher: An Intelligent Resume Ranking System
Prasad Dhobale, Vinayak Bhoir, Abhijeet Vyavhare, Pratik Yelkar, Pramod A. Dharmadhikari · 2025
The growing complexity of recruitment processes necessitates advanced solutions for resume screening and ranking. In this paper, we present ResuMatcher, an AI-powered resume ranking system that leverages the power of Large Language Models (LLMs) for semantic understanding. Unlike traditional keyword-based systems, ResuMatcher evaluates the context and meaning behind terms in resumes and job descriptions, providing a more accurate and efficient matching process. Using models such as BERT and transformer-based architectures, ResuMatcher can assess candidate qualifications by considering the underlying semantics of job requirements and candidate skills. The system was evaluated on a diverse dataset across industries such as IT, healthcare, and finance, and showed a significant improvement in both accuracy and efficiency compared to conventional Applicant Tracking Systems (ATS). Results from user testing demonstrated high satisfaction rates among both job seekers and recruiters, highlighting ease of use, relevance of matches, and efficiency gains in the hiring process