A Skill-Based Resume Ranking System for Efficient Candidate Selection

Abstract—In today's competitive job market, companies are inundated with resumes from a vast pool of applicants. Traditional methods of sorting through resumes can be time-consuming and ineffective, making it difficult to identify the best candidates. In this paper, we propose a skill-based resume ranking system that uses natural language processing and machine learning techniques to automate the resume screening process. Our system extracts the skills from both job postings and resumes, and then ranks resumes based on their relevance to the job requirements. We evaluate the performance of our system by comparing it to traditional manual screening methods and show that our system can accurately identify the most qualified candidates in a fraction of the time. Our proposed system can greatly enhance the recruitment process by improving the efficiency and accuracy of candidate selection