Ai-driven Resume Analysis And Job-specific Resume Optimization System

Abstract

This Paper Presents An AI-driven System For Analyzing Resumes And Optimizing Them To Match Specific Job Descriptions. Modern Recruitment Relies Heavily On Applicant Tracking Systems (ATS), Which Filter Candidates Based On Keyword Relevance And Semantic Alignment. Many Qualified Candidates Are Inadvertently Eliminated Due To Poor Resume Formatting Or Misaligned Content. The Proposed System Employs Natural Language Processing (NLP) And Machine Learning (ML) Techniques, Including Named Entity Recognition (NER) For Structured Information Extraction And Sentence-BERT (SBERT) Embeddings With Cosine Similarity For Semantic Matching Between Resumes And Job Descriptions. The System Identifies Skill Gaps, Computes A Quantitative Match Score, And Provides Personalized Optimization Recommendations Including Keyword Suggestions, Content Rephrasing, And ATS Formatting Guidance. A Flask-based REST API Enables Real-time Interaction. Experimental Evaluation Demonstrated An 88.40% Matching Accuracy, With 85% Of Users Reporting Improved Resume Quality. The System Offers An Accessible, Intelligent, And Explainable Solution To Bridge The Gap Between Job Seekers And Automated Recruitment Processes.

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