Intelligent Job Recommendation with NLP and Densitybased Clustering

The need for effective and tailored job recommendation systems has driven research into advanced algorithms that match candidates to relevant job roles. This study presents a system utilizing Natural Language Processing (NLP) and density-based clustering techniques, including BERT, HDBSCAN, DBSCAN, and K-means. The system processes user resume data, encompassing skills, certifications, and experience, extracted using PDFMiner, spaCy, Tesseract OCR, and other tools. By encoding skills through Sentence- BERT and clustering them with HDBSCAN, the system effectively groups related competencies, enabling precise job role recommendations and tailored interview questions. The proposed approach simplifies job searches and enhances user preparation by aligning user skills with market demands and providing relevant interview content. This research highlights the system's utility in career development through comprehensive resume analysis and job matching.