ResuMatch: An Interpretable NLP-Based Framework for Semantic Resume Job Description Matching in Automated Recruitment Systems

This paper has introduced ResuMatch, an artificial intelligencebased system that helps in automating the semantic mapping of candidate rsums to job specifications based on structured extraction, contextual semantic comparison and multi dimensional scoring algorithm. The system identifies conceptually meaningful matching between docu-ments instead of superficial lexical matching by using vector based similarity computations, thus providing candidate evaluations that are accurate and fair. Besides ranking, ResuMatch also provides transparent and rationale based explanations by explicitly showing matched competencies, missing skills and the experiential evidence of proficiencies. Apart from aiding recruiters, candidates also get considerable advantages from receiving clear, actionable advice on how to increase their chances of being selected for a future job. The framework shortens screening cycles, increases inter rater ranking consistency, and decreases the dependence on subjective heuristic judgment. Future work includes multilingual rsum processing, ATS integration, and the development of real time adaptive feedback modules. In sum, ResuMatch is a demonstration of how AI powered assessment tools can lead to increased trust, higher accuracy, and faster processing in the contemporary recruitment scenario.

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