Current Volume 9
An AI-Based Applicant Tracking System for Resume Analysis and Skill Gap Prediction Using NLP Techniques
Abstract
The rapid growth of online recruitment platforms has increased the number of job applications received by organizations, making manual resume screening time-consuming and inefficient. Traditional recruitment methods often rely on keyword matching and manual evaluation, which may lead to inaccurate candidate selection and human bias. This research proposes an AI-Based Applicant Tracking System (ATS) for Resume Analysis and Skill Gap Prediction using Natural Language Processing (NLP) techniques. The system uses TF-IDF vectorization and cosine similarity algorithms to compare resumes with job descriptions and calculate candidate compatibility scores. A skill gap prediction module identifies missing skills required for specific job roles. The proposed system also includes a dashboard analytics module that displays recruitment metrics such as total resumes analyzed, highest match score, average compatibility score, and candidate ranking. In addition, the HOT-Fit model is used to evaluate the system from human, organizational, and technological perspectives. The proposed framework improves recruitment efficiency, reduces manual effort, supports transparent hiring decisions, and enhances candidate-job matching accuracy through AI-driven analysis.
Keywords
Applicant Tracking System (ATS), Natural Language Processing (NLP), Resume Analysis, TF-IDF, Cosine Similarity, Skill Gap Prediction.
Citations
IRE Journals:
R. Raghavendra, Vijay B "An AI-Based Applicant Tracking System for Resume Analysis and Skill Gap Prediction Using NLP Techniques" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2413-2423 https://doi.org/10.64388/IREV9I11-1717992
IEEE:
R. Raghavendra, Vijay B
"An AI-Based Applicant Tracking System for Resume Analysis and Skill Gap Prediction Using NLP Techniques" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717992
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12