- Monday, Apr 6th, 2026
| TITLE | An Explainable and Semantic AI-Based Applicant Tracking System |
|---|---|
| ABSTRACT | Artificial intelligence's explosive growth in hiring and professional advancement has fundamentally changed how employability is evaluated and improved. The proposed system automatically analyzes resumes using NLP algorithms like Spacy and PyMuPDF that extract structured data about the candidates' skills, education, projects, and experience. It further makes use of machine-learning-based gap analysis and semantic similarity models, TF-IDF, and BERT, for the evaluation of candidature fit for specific job requirements and generates a Skill Match Score and Readiness Score. Apart from ranking, the platform extends individually customized learning path suggestions that close the identified gaps; hence, the advantages are extended to both recruiters and candidates. The major innovation is integrating Explainable AI, which provides understandable and clear reports on how rankings, scores, and recommendations are computed. Reviewing the literature on AI-enabled recruitment systems, the paper discusses the novelty of bringing together LLM-driven assessment with XAI-enabled interpretability into a single architecture of ATS. |
| AUTHOR | Vignesh Master of Computer Applications, CMR Institute of Technology, Bangalore, India |
| VOLUME | 13 |
| DOI | DOI: 10.15680/IJARETY.2026.1302027 |
| 27_An Explainable and Semantic AI-Based Applicant Tracking System.pdf | |
| KEYWORDS | |
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