Authors : Dr. R. Kaviarasan; Y. Neha

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/3sfn36k4

DOI : https://doi.org/10.38124/ijisrt/25jun284

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : A Resume Inflation System proposed in this research is a progressive, computer-based framework engineered to optimise the personnel recruitment cycle by facilitating hands-free operation and performance review of job seekers’ resumes. In the recruitment cycle, hiring managers are often flooded with numerous resumes from applicants. This large volume of applicants can lead to difficulties in selecting candidates for various positions. However, there is no open-source application that assists in shortlisting resumes for a particular position directly using the job description. This inspires us to introduce a methodology for a resume Inflation system. The resume Inflation system mitigates these issues by introducing the initial review activity, which reduces the time spent on manual screening by up to 80%. This Inflation system employs machine learning models and NLP to proficiently inspect resumes, extract relevant skills, experiences, and qualifications, and match them against specific job descriptions with maximum accuracy. The procedure ensures that the most promising applicants are prioritised for further rounds of evaluation, which enhances the standards of shortlisted applicants and decreases the risk of mismatch. The Resume Inflation System ensures a fairer recruiting procedure and nurtures diversity in the hiring process. This latest application not only saves time but also increases the efficiency and accuracy of candidate selection.

Keywords : ML Models, NLP, AI, Performance Review, Inflation System, Computer-Based Framework, Application Tracking System (ATS).

References :

1. Elham Albaroudi, Taha Mansouri, "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", in AI journal Volume 5, Issue 1, MDPI 2024.
2. Ashvini Chavan, Nikita Tatewar, "AI Resume Analyzer", in the International Journal of Creative Research Thoughts (IJCRT), December 2023.
3. Yiou Lin, Hang Lei, "Machine learned resume-job matching solution", in International Conference on Artificial Intelligence and Data Engineering, 2021.
4. Hameed, K.; Arshed, N.; Yazdani, N.; Munir, M. On globalization and business competitiveness: A panel data country classification. Stud. Appl. Econ. 2021, 39, 1–27. [CrossRef]
5. Farida, I.; Setiawan, D. Business strategies and competitive advantage: The role of performance and innovation. J. Open Innov. Technol. Mark. Complex. 2022, 8, 163. [CrossRef]
6. Yi-Chi Chou, Han-Yen Yu, " Based on the application of AI technology in resume analysis and job recommendation " in IEEE International Conference on Computational Electromagnetics, 2020, pp. 293-296.
7. J. Singh and S. Singh, “A natural language processing approach to extracting information from resumes,” in 5th International Conference on Industrial Engineering and Applications (ICIEA), 2018.
8. Rimitha, S. R., Abburu, V., Kiranmai, A., & Chandrasekaran, K “Ontologies to Model User Profiles in Personalized Job Recommendation, ” In 2018 IEEE Distributed Computing, VLSI,Electrical Circuits and Robotics (DISCOVER), pp. 98-103, 2018.
9. Kim, S., et al. "Personalized Recommendation Systems for Job Vacancies." IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 6, 2019, pp. 1175-1187.
10. Amato F., Boselli R., Cesarini M., et al. Challenge: Processing web texts for classifying job offers[C]//Semantic Computing (ICSC), 2015 IEEE International Conference on. IEEE, 2015: 460-463.