An Intelligent Resume Analysis and Job Recommendation System Using NLP

Recruitment systems face increasing challenges due to the diversity of résumé formats, lack of personalized guidance for job seekers, and the persistence of human bias in candidate evaluation. Traditional systems often fail to accurately extract essential information, assess résumé quality, and provide actionable recommendations aligned with labor market requirements. This paper presents an intelligent résumé analysis and job recommendation system based on Natural Language Processing (NLP) and transparent rule-based logic. The system extracts structured information from text-based PDF résumés using PDFMiner3, PyResParser, and spaCy. Résumé quality is evaluated using a rule-based scoring mechanism, while skill gap analysis is performed through explicit comparison between extracted skills and predefined domain requirements. Based on this analysis, the system generates personalized job and training recommendations. The system was evaluated through laboratory testing and real-world field evaluation in collaboration with Human Resources specialists. Experimental results show 90% skill extraction accuracy, 92% job domain classification accuracy, 89% skill gap detection accuracy, and over 91% relevance in job and training recommendations. The findings demonstrate that the proposed system provides an effective, interpretable, and fair solution for intelligent recruitment and career development