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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

AI-DRIVEN TALENT MATCHING SYSTEM USING BERT AND RECOMMENDATION ALGORITHMS

Umesh Shingare Saurav Sultane Om Autade Om Taskar Kalpesh Wagh

Prof D. S. Shingate

Department of Information Technology / MET’s Bhujbal Knowledge City IoE / Nashik India

Article Status

Plagiarism Passed Peer Reviewed Open Access

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Abstract

In today’s competitive job market, organizations struggle with the overwhelming volume of resumes received for each job posting, making manual screening inefficient and prone to bias. This research directly addresses these pain points by presenting an AI-Based Resume Shortlisting and Job Recommendation System. The system’s main thesis is to leverage advanced AI and ML technologies to automate and optimize recruitment, enabling faster, fairer, and more accurate hiring decisions. The system is designed to serve both candidates and HR administrators through a dual-interface architecture. Candidates can register, upload their resumes, and receive real-time evaluation in the form of a resume score, personalized skill enhancement suggestions, and job recommendations aligned with their profile. The system also enables candidates to directly apply for relevant job roles, thereby streamlining the job search process. On the administrative side, HR users can create and manage job postings, analyze candidate resumes, and leverage the AI model to automatically shortlist the most suitable applicants based on predefined criteria.

Keywords—Artificial Intelligence, Feature Extraction, Cosine Similarity, Resume Screening, Transformer Models / BERT, Recruitment Automation, Candidate Evaluation.

How to Cite this Paper

Shingare, U., Sultane, S., Autade, O., Taskar, O. & Wagh, K. (2026). AI-Driven Talent Matching System Using BERT and Recommendation Algorithms. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.016

Shingare, Umesh, et al.. "AI-Driven Talent Matching System Using BERT and Recommendation Algorithms." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.016.

Shingare, Umesh,Saurav Sultane,Om Autade,Om Taskar, and Kalpesh Wagh. "AI-Driven Talent Matching System Using BERT and Recommendation Algorithms." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.016.

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References


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  • Karan Patel, Deepika Singh, and Ravi Nair. (2023). Automated Job Recommendation System Using Machine Learning and User Profiling. Develops a recommendation engine that suggests jobs based on resume data, interests, and candidate skill sets. [3] D. D. Author et al., “Conference Paper Title,” in Proc. IEEE Conf., Year, pp. 1–6.

  • Ahmed Mohamed, Fatima Khan, and John Lewis. (2022). Intelligent Hiring System Using Django Framework and Machine Learning Algorithms. Integrates Django web frameworkwith AI to streamline the recruitment and candidate evaluation process.



  • Vikas Kumar, Sneha Joshi, and Arjun Rao. (2024). A Study on Resume Classification Using Deep Learning Approaches. Compares deep learning models like CNN and BERT for skill extraction, job fit prediction, and automated

  • Aditi Singh and Meena Iyer. (2023). AI in Human Resource Management: An Overview of Recruitment Automation. Explores how artificial intelligence transforms HR operations by improving decision- making in hiring.

  • Rajesh Gupta and Harshita Malhotra. (2022). TextMining Techniques for Resume Screening: Challenges and Opportunities. Analyzes NLP-based resume parsing techniques and their role in automating candidate evaluation.

  • Li Chen, Maria Santos, and David (2024). Design and Implementation of a Job Matching Platform Using Python and SQLite. Describes the backend development and database structure for AI- driven job recommendation systems.

  • George Thomas and Aishwarya Das. (2023). Skill Gap Analysis Using Machine Learning for Career Recommendation. Proposes an AI model that identifies missing skills in candidate profiles and provides personalized improvement advice.

  • R. Raoand K. Ramesh (2025). Django-Based Web Application for Automated Recruitment Process. Presents a full stack implementation for resume upload, job posting, and candidate ranking using AI models.

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Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
  • Authors retain copyright.
  • Peer Review Type: Double-Blind Peer Review
  • Published on: May 03 2026
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