Randomized trial demonstrates improved candidate assessment in job applications using AI analysis systems, indicating more effective hiring processes.
This research paper presents an AI-powered resume analysis system that improves traditional Applicant Tracking System (ATS) screening using Natural Language Processing (NLP) and semantic similarity techniques. The proposed system utilizes RoBERTa transformer models to evaluate contextual similarity between resumes and job descriptions instead of relying solely on keyword matching. The framework also integrates Named Entity Recognition (NER)-based skill gap analysis to identify missing competencies and provide actionable recommendations for candidates. The system supports secure PDF resume processing, ATS-compatible scoring, and interpretable insights through a user-friendly interface. Experimental observations demonstrate that semantic similarity-based evaluation provides more accurate and context-aware candidate assessment compared to conventional keyword filtering methods.
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Shaikh et al. (2026) studied this question.
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