A New Approach on AI-Based Resume Analysis and Feedback System

Traditional Applicant Tracking Systems (ATS) are based mostly on filtering based on keywords and usually do not

provide the semantic meaning, structural quality, and contextual relevance of resumes. Such a restriction causes incorrect

analysis of candidates and lower recruitment transparency. In order to deal with these issues, this paper suggests a resume

analysis and feedback system based on AI which incorporates all three concepts of semantic similarity, relevance, and heuristic

structural analysis into an integrated hybrid framework. This suggested system presents a Hybrid Semantic–Keyboard–Heuristic

(HSK) scoring split, a hybrid of rule-based and contextual evaluation to offer balanced resume scores. The overall

correspondence between resumes and job descriptions is captured with the help of semantic similarity, and the necessary skills

are identified with the help of key words matching. The heuristic aspect considers the resume format, completeness and

readability. This is an operation that gives a weighted average of these elements to get the final score. Moreover, it will use a

Gemini-based Large Language Model to provide customized section-by-section feedbacks that allow candidates to effectively

amend their resumes. The results of the experiment show better accuracy, interpretability and correspondence with human

assessment than traditional ATS methods. The suggested system is a clear, understandable, and easy-to-use solution to resume

evaluation in the current era.