An AI-Based Applicant Tracking System for Resume Analysis and Skill Gap Prediction Using NLP Techniques

@article{1717992,
      author = {R. Raghavendra, Vijay B},
      title = {An AI-Based Applicant Tracking System for Resume Analysis and Skill Gap Prediction Using NLP Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {2413-2423},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717992.pdf},
      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.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717992}
  }