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 | LLM Powered Resume Relevance Engine for Automated Job Matching |
|---|---|
| ABSTRACT | The recruitment process has become increasingly challenging due to the large volume of job applications received by organizations. Traditional Applicant Tracking Systems (ATS) primarily rely on keyword-based matching techniques, which often fail to accurately evaluate candidate suitability because they lack contextual understanding. To address these limitations, the Resume Matcher System is developed as an AI-powered platform that leverages Artificial Intelligence (AI), Natural Language Processing (NLP), semantic analysis, and Large Language Models (LLMs) to improve resume evaluation and job matching accuracy. The system enables users to upload resumes and job descriptions, automatically extracts and structures relevant information, and performs semantic similarity analysis to determine candidate-job compatibility. Advanced features such as resume parsing, section detection, semantic score generation, skill gap analysis, resume strength and weakness identification, and AI-powered resume tailoring help users optimize their resumes according to job requirements. Additionally, the system provides career assistance through interview question generation, LinkedIn headline generation, cover letter creation, outreach message generation, and professional PDF resume generation. The application is developed using Next.js, FastAPI, PostgreSQL, and Ollama-based language models. By combining semantic understanding with explainable AI recommendations, the Resume Matcher System enhances recruitment efficiency, reduces manual screening effort, and helps candidates improve their chances of securing relevant job opportunities. The proposed system provides an intelligent, scalable, and user-friendly solution for modern recruitment and career development processes. |
| AUTHOR | DR. SHAILESH BENDALE, AKASH BHAGAT, ATHARVA JOSHI, DINESH KASHIWANT, AJINKYA LADKAT Guide, NBN Sinhgad Technical Institutes Campus, Pune, India Student, NBN Sinhgad Technical Institutes Campus, Pune, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406069 |
| pdf/69_LLM Powered Resume Relevance Engine for Automated Job Matching.pdf | |
| KEYWORDS | |
| References | 1. Panagiotis Skondras, George Psaroudakis ,Panagiotis Zervas , Giannis Tzimas "Efficient Resume Classification through Rapid Dataset Creation Using ChatGPT". 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA). 2. Chuan Qin,Le Zhang, Yihang Cheng, Rui Zha, Dazhong Shen, Qi Zhang, Xi Chen, Ying Sun, Chen Zhu, Hengshu Zhu, Hui Xiong ,"A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics".arXiv:2307.03195v2 [cs.CY] 6 May 2024. 3. P. Varalakshmi , N. Meena Kumari Bugatha,"AI-Powered Resume Based QA Tailoring for Success in Interviews", 2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). 4. Frank P.-W .Lo,Jianing Qiu2, Zeyu Wang1 ,Haibao Yu3, Yeming Chen4, Gao Zhang5,Benny Lo - "AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening" , arXiv:2504.02870v2 5. Dhanalakshmi R V ,Shivashish Gour , Shashank M , Sushmitha K , K J Nithin , Keerthana S N - "AI-Powered Resume Screening System using NLP and Machine Learning" , 3rd International Conference on Inventive Computing and Informatics (ICICI). 6. M.K.Vijaymeena , K.Kavitha - "A SURVEY ON SIMILARITY MEASURES IN TEXT MINING" ,Machine Learning and Applications: An International Journal (MLAIJ) Vol.3, No.1 7. Dena F. Mujtaba ,Nihar R. Mahapatra - "Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions" , arXiv:2405.19699v3 [cs.CY] |
