An AI-Powered Resume Evaluation and Applicant Tracking System Optimization Framework Using Machine Learning and Natural Language Processing

Abstract: The recruitment landscape has been reshaped by the widespread deployment of Applicant Tracking Systems (ATS), which automatically filter the overwhelming volume of applications received for every advertised vacancy. A large share of qualified candidates are nonetheless rejected before any human review because their resumes are poorly aligned with machine-readable parsing conventions and role-specific terminology. This study presents an intelligent resume evaluation and ATS optimization framework that couples natural language processing with supervised machine learning to quantify the suitability of a curriculum vitae against a target job description and to deliver actionable, personalized improvement guidance. The proposed pipeline ingests heterogeneous document formats, performs robust text extraction and normalization, derives semantic and lexical features, and produces a calibrated compatibility score through a soft-voting ensemble of classifiers. A skill-gap analyzer cross-references extracted competencies against a curated knowledge base to surface missing keywords and formatting deficiencies. The system was implemented as a modular web application with a Node.js and React front end and a Python analytical back end. Experimental evaluation on a corpus of annotated resume–vacancy pairs demonstrated an accuracy of 92.7% and an F1-score of 0.921, surpassing four competitive baseline classifiers. Beyond predictive performance, the framework reduced the average number of unaddressed keyword gaps per resume by a substantial margin in a controlled user study. The principal contributions are a reproducible feature-engineering scheme, an interpretable scoring mechanism, and an end-to-end deployable architecture suitable for real-world career-support settings.

Keywords: Resume evaluation, Applicant Tracking System, Natural Language Processing, Machine Learning, Ensemble Learning, Skill-Gap Analysis, Recruitment Automation, Text Mining