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 | A Survey On “LLM Powered Resume Relevance Engine for Automated Job Matching” |
|---|---|
| ABSTRACT | This project creates an LLM-powered resume releveance engine that outperforms conventional NLP-based systems using contextual understanding. It addresses the shortcomings of current NLP techniques in terms of explainability, accuracy, and fairness by building upon them as references. Through enhanced semantic similarity and interpretable relevance scoring, the system improves resume-job matching using cutting-edge LLMs. Results from experiments show how well it works to offer recommendations for hiring that are transparent, equitable, and supprted by data. By advancing AI-driven talent acquisition tools, this research opens the door to automated hiring solutions that are more dependable and transparent. |
| TITLE | |
| AUTHOR | DR. SHAILESH BENDALE, AKASH BHAGAT, ATHARVA JOSHI, DINESH KASHIWANT, AJINKYA LADKAT |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311051 |
| pdf/51_A Survey On “LLM Powered Resume Relevance Engine for Automated Job Matching”.pdf | |
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