Authors : P. Leela Sesha Balaji; A. Gopika Anjali; G. Sai Satya Ganapathi; K. Nithish; D. Keerthi Raju

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/w92um5nv

Scribd : https://tinyurl.com/2kcay4bx

DOI : https://doi.org/10.38124/ijisrt/26mar1237

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

Abstract : The recruitment process in the current day is being subjected to immense pressure since companies are getting a surge of resumes for each available job. Listing and comparing all the resumes manually consume a lot of time and may result in unfair or prejudice results. For this purpose, the Smart Resume Filtering andTailoring System has been created. It is an artificial intelligence-based system beneficial for recruiters and job seekers alike. Job applicants can input their resume and job advertisement to obtain a match score, missing skills list, and suggestions on resume improvement. The system also provides the ability for a recruiter to input multiple resumes for a single job posting and automatically rank the applicants based on the job requirement. The system utilizes simple Natural Language Processing (NLP) techniques, text mining, and rules-based verification to determine match scores. It also offers a safe login and data storage system. The project saves time, enhances the accuracy of recruitment, and assists applicants in knowing how to enhance their resumes.

Keywords : Resume Analysis, Semantic Matching, Skill Extraction, Job Ranking, Natural Language Processing, FastAPI, Candidate Evaluation.

References :

1. M. Saatcı, R. Kaya, and R. Ünlü, "Resume Screening with Natural Language Processing (NLP)," Alphanumeric Journal, vol. 12, no. 2, pp.112,2024.Available: https://dergipark.org.tr/en/download/article- file/4159614 .
2. P. Pezeshkpour, H. Iso, T. Lake, N. Bhutani, and E. Hruschka, "Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks," arXiv, Nov. 2023. Available: https://arxiv.org/abs/2311.06383 .
3. C. Gan, Q. Zhang, and T. Mori, "Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening," arXiv, Jan. 2024.Available: https://arxiv.org/abs/2401.08315 .
4. F. P.-W. Lo, J. Qiu, Z. Wang, H. Yu, Y. Chen, G. Zhang, and B. Lo, "AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening," arXiv, Apr. 2025.Available: https://arxiv.org/abs/2504.02870 .
5. M.Blessing," AI-Powered Resume Screening: Benefits and Challenges," ResearchGate,Feb.2025.Available: https://www.researchgate.net/publication/3886 88179_AIPowered_Resume_Screening_Benefi ts_and_Challenges .
6. "Automated Resume Parsing: A Natural Language      Processing Approach," ResearchGate, Aug. 2023. Available: https://www.researchgate.net/publication/3763 28242_Automated_Resume_Parsing_A_Natur al_Language_Processing_Approach .
7. "Resume Parser Using NLP," International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 9, pp. 1–6,Sep.2024.Available: https://ijarcce.com/wpcontent/uploads/2024/09/IJARCCE.2024.1395.pdf.
8. "Resume Summarizer and Job Description Matcher Using Natural Language Processing and SpaCy," ResearchGate, Aug. 2025. Available: https://www.researchgate.net/publication/375601462_RESUME_SUMMARIZER_AND_JO B_DESCRIPTION_MATCHER_USING_NAT URAL_LANGUAGE_PROCESSING_AND_ SPACY .
9. "NLP-Powered Resume Matching for Recruitment," International Journal of Future Management and Research, vol. 6, no. 1, pp. 1– 6,Jun.2024.Available: https://www.ijfmr.com/papers/2024/6/31742.pd f
10. "Resume Parser Using Hybrid Approach to Improve the Accuracy of Resume Screening," Authorea, Mar.2023.Available: https://www.authorea.com/doi/pdf/10.22541/au.168170278.82268853 .