An Explainable Rule-Based Resume-Job Description Skill Matching System with LLM-Assisted Feedback

Abstract

Automated resume screening and Applicant Tracking Systems (ATS) increasingly incorporate machine learning components and neural language models. While deep learning methods can capture complex linguistic patterns, their computational overhead, potential opacity, and sensitivity to unvetted training distributions pose practical and ethical challenges for lightweight, transparent academic deployments. In this paper, we describe an open, lightweight, and explainable resume–job description matching system that couples deterministic rule-based information extraction with optional Large Language Model (LLM) qualitative feedback. The core pipeline processes PDF, DOCX, and plain-text resumes, detects functional sections, extracts technical competencies using an auditable 108-skill taxonomy, and evaluates resume–job description compatibility using explicit set operations and a bounded 100-point heuristic score covering Skills, Experience, Projects, Education, and Certifications. To benchmark the pipeline without making real-world recruitment claims, we developed a deterministic synthetic evaluation harness containing 100 resumes, 20 job descriptions, 320 evaluation pairs, and 20 candidate ranking cohorts. Under the synthetic benchmark, the system achieved F1-scores of 0.791 for skill extraction, 0.827 for education, 0.772 for experience, 0.759 for projects, and 0.865 for missing-skill identification. Robustness testing exposed 0.0% recall when experience titles and dates were separated across lines and complete section collapse under unsupported headings. Synonym normalization increased skill recall from 0.896 to 0.968 under controlled conditions. The results characterize the behavior and limitations of the proposed deterministic baseline and provide a reproducible basis for future evaluation with real, anonymized and human-annotated resume data. The benchmark also reports multi-format consistency and controlled baseline comparisons to expose formatting sensitivity and vocabulary-related error patterns.