International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Powered Resume Screening and Job Matching |
|---|---|
| ABSTRACT | The growing volume of job applications has increased the need for automated resume screening systems that can efficiently evaluate candidate profiles. This paper presents an intelligent Resume Screening Application that integrates Natural Language Processing (NLP), Machine Learning (ML), and Groq Llama-based Large Language Models (LLMs) to automate resume analysis. The system achieves 95% accuracy in job category prediction across 33+ domains and provides ATS compatibility scoring. Using TF-IDF vectorization, cosine similarity, and AI-driven analysis, the application improves screening speed and accuracy while addressing challenges such as skill extraction, keyword matching, and resume format variations. The proposed system helps reduce recruitment time, minimize bias, and enhance candidate-job matching. |
| AUTHOR | SHALINI L, DR. BASAVESHA D M. Tech Student, Dept. of CSE, Shridevi Institute of Engineering and Technology, Tumakuru, Karnataka, India Professor & HOD, Dept. of CSE, Shridevi Institute of Engineering and Technology, Tumakuru, Karnataka, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406028 |
| pdf/28_AI-Powered Resume Screening and Job Matching.pdf | |
| KEYWORDS | |
| References | [1] Arshad Shaikh et al., "Resume Parser and Summarizer," International Journal of Computer Applications, Vol. 176, No. 24, 2023, pp. 1–6. [2] Surendiran B et al., "Resume Classification Using ML Techniques," International Research Journal of Engineering and Technology (IRJET), Vol. 10, Issue 2, 2023, pp. 18–25. [3] Federico Retyk et al., "Résumé Parsing as Hierarchical Sequence Labeling: An Empirical Study," Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023, pp. 157–169. [4] V. Lai et al., "CareerMapper: An Automated Resume Evaluation Tool," Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, No. 10, 2023, pp. 12567–12574. [5] S. Bharadwaj et al., "Resume Screening using NLP and LSTM," International Journal of Innovative Technology and Exploring Engineering, Vol. 9, No. 3, 2022, pp. 45–52. [6] Sergio Zavota et al., "An End-to-End Framework for Resume Information Extraction," Expert Systems with Applications, Vol. 200, 2022, Article 117050. |
