AI-powered job matching system for job seekers and recruiters

Abstract

This study examines the development of an Intelligent Job Matching System (IJMS) using Laravel. Traditional recruitment methods often rely on manual keyword-based searches, leading to inefficient matches between job seekers and employers. This project aims to create a more effective system that extracts and analyses relevant information from CVs and job descriptions to improve job-candidate matching. The system integrates Optical Character Recognition (OCR) for text extraction from images and PDFs, enabling employers to upload job vacancies in various formats. A structured matching algorithm is implemented to ensure accurate job recommendations based on extracted details. To enhance flexibility, efficiency, and iterative improvements, the Agile methodology is followed throughout the development process. The system is designed with a user-friendly interface, guided by wireframes created in Figma, which include login pages, registration pages for both job seekers and employers, dashboards, and job posting functionalities. By automating job matching, reducing manual filtering, and streamlining the recruitment process, this system aims to significantly improve the job search experience for both job seekers and employers. Paragraphs and references are not allowed. Abbreviations can be used if they have been defined previously.