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 | Implementation of a Resume Analyser and Job Matching System Using Artificial Intelligence |
|---|---|
| ABSTRACT | The Resume Analyzer and Job Matching System using Artificial Intelligence is an advanced web-based application designed to automate, optimize, and modernize the recruitment process by analyzing resumes and matching candidates with appropriate job roles. In today’s competitive job market, organizations receive a large volume of resumes for each job opening, making manual screening time-consuming, inefficient, and prone to human bias and errors. To address these challenges, the proposed system leverages Artificial Intelligence techniques, particularly Natural Language Processing (NLP) and Machine Learning (ML), to extract meaningful information from resumes in an automated and structured manner. Using intelligent algorithms, the extracted resume data is compared with job requirements to compute a similarity or matching score, indicating how well a candidate fits a position. Based on this score, the system provides job recommendations and assists recruiters in efficiently shortlisting suitable applicants. The implementation of machine learning models ensures continuous improvement in matching accuracy as more data is processed. By minimizing manual intervention, the system reduces recruitment time, enhances decision-making accuracy, promotes fairness by limiting bias, and ensures a transparent evaluation process. |
| AUTHOR | C.PRAKASH NARAYANAN, SHARMILA K, VETRIMANI M, SACHIN V, NAVEEN KUMAR S Assistant Professor, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India UG Scholar, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404064 |
| pdf/64_Implementation of a Resume Analyser and Job Matching System Using Artificial Intelligence.pdf | |
| KEYWORDS | |
| References | 1. Kumar, A. & Gupta, S. (2018). “Automated Resume Parsing using NLP Techniques.” International Journal of Computer Applications.This paper presents natural language processing methods for extracting structured data from unstructured resumes. The study discusses tokenization, entity extraction, and skill classification, which are foundational for automated screening systems. It supports the use of NLP in resume analysis. 2. Zhang, Y., Li, H., & Wang, J. (2019). “Intelligent Job Recommendation using Machine Learning.” IEEE Access.The authors describe a job recommendation framework that applies machine learning to match candidate profiles with job descriptions. Their evaluation shows improved matching accuracy compared to keyword-based approaches, reinforcing the need for AI in recruitment. 3. Sharma, P. & Mehta, N. (2020). “Semantic Skill Matching in Recruitment Systems.” Journal of AI Research.This research focuses on semantic similarity measures for matching candidate skills with job requirements. It highlights the limitations of basic keyword matching and demonstrates how semantic embeddings enhance matching effectiveness. 4. Patel, R., Desai, T., & Shah, M. (2019). “Recruitment Automation using NLP and ML Models.” International Journal of Engineering & Technology.The study discusses integrating NLP with machine learning classifiers for automated candidate shortlisting. It provides evidence that AI reduces human bias and improves efficiency in large-scale recruitment. 5. Liu, S. & Chen, D. (2021). “Deep Learning Approaches to Resume Classification.” Journal of Information Technologies.This paper explores convolutional and recurrent neural networks for classifying resumes into job domains. It demonstrates that deep learning outperforms traditional machine learning in extracting contextual resume features. 6. Reddy, K. & Kumar, V. (2021). “AI Driven Recruitment Analytics.” International Journal of Data Science.This work introduces recruitment analytics dashboards powered by AI scoring. It highlights how matching scores and skill distribution insights assist recruiters in data-driven decision making. 7. Ahmed, F., Ali, Z., & Khan, M. (2022). “Job Matching System using Semantic Analysis.” IEEE Transactions on Knowledge and Data Engineering.The authors propose a semantic matching model using word embeddings for evaluating candidate-job compatibility. The system shows enhanced performance over traditional TF-IDF based matching. |
