AI-Driven Intelligent System for Personalized Job Recommendations Using Real-Time Skill Identification and Industry Trend Analysis

Navigating today’s job market is challenging, especially for fresh graduates and professionals seeking the right opportunities. Traditional keyword-based job search methods often result in mismatches, making it difficult for candidates to find suitable roles and for employers to connect with the right talent. To address this, we propose an intelligent job recommendation system that leverages structured and unstructured data from job portals, recruitment databases, and candidate inquiries to provide highly relevant matches. Our approach integrates Named Entity Recognition (NER) with fuzzy matching to standardize skills, ensuring better alignment between candidate profiles and industry demands. By incorporating Levenshtein Distance for spelling correction and frequency-based skill analysis, we refine skill extraction for more precise recommendations. Additionally, Sentence-Bert (SBERT) captures semantic similarities between job descriptions and candidate qualifications, while a multi-armed bandit (MAB) algorithm optimizes job ranking. To enhance accessibility, MarianMT enables multilingual support. Experimental results show significant improvements in job matching accuracy, offering an efficient, inclusive, and scalable solution for modern recruitment challenges.