Authors :
Nikhil Modi; Aaditi Indalkar; Aryan Kapole; Saara Khamkar; Madhavi A. Indalkar
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/2z833e95
Scribd :
https://tinyurl.com/y9r9f9jc
DOI :
https://doi.org/10.5281/zenodo.14608655
Abstract :
This study introduces the Next-Gen Talent
Matching System, an innovative JD-based CV filtering
web application designed to transform the recruitment
process by leveraging Large Language Models (LLMs)
and OpenAI technologies. Unlike traditional systems that
rely on skill-based c filtering, this system focuses on job
description (JD)-based filtering, providing greater
accuracy and relevance in candidate selection. By enabling
users to securely submit CVs, the system stores data in a
MongoDB database, allowing HR administrators to access
and match CVs based on semantic analysis. Using LLMs,
the system analyses job descriptions and CVs to rank
candidates according to how well they align with the job
requirements, taking into account skills, experience, and
qualifications. This approach enhances the efficiency of the
recruitment process by automating initial screening,
reducing human bias, and providing real-time feedback to
candidates. The Next-Gen Talent Matching System not
only improves the quality of candidate shortlisting but also
integrates with existing HR platforms and scales to handle
both small and large recruitment needs. Through its AI-
driven, data-centric approach, the system serves as a
powerful tool for modern recruitment, significantly
reducing the time and effort required by HR professionals
while ensuring more accurate and unbiased hiring
decisions.
Keywords :
JD-based Filtering, LLMs, OpenAI, AI-driven Recruitment, Semantic Analysis, Bias Reduction, Automated Candidate Matching.
References :
- Gupta and P. Bhalla, "A study of E-Recruitment From the Perspective of Job Applicants," International Journal of Advanced Research in Computer Science, vol. 9, no. 1, pp. 150-156, 2018.
- P. Srivastava and N. Rathod, "An efficient algorithm for ranking candidates in e-recruitment system," International Journal of Computer Applications, vol. 125, no. 11, pp. 25-29, 2015.
- S. D. Arora and R. Dhiman, "Artificial Intelligence in Human Resource Management," Journal of Strategic Human Resource Management, vol. 8, no. 3, pp. 45-52, 2019.
- J. Singh and S. Patel, "AI Based Suitability Measurement and Prediction Between Job Description and Job Seeker Profiles," in Proceedings of the International Conference on Artificial Intelligence and Machine Learning, Jaipur, India, 2020, pp. 180188.
- R. Kumar and A. Gupta, "Job opportunities for LIS professionals in India: A study based on Online Job portals," Journal of Library and Information Science, vol. 10, no. 2, pp. 85-95, 2020.
- S. Sharma and M. Mehta, "Real-Time Resume Screening Using NLP and Token-Based Indexing," Journal of Emerging Trends in Computing and Information Sciences, vol. 7, no. 4, pp. 101-107, 2016.
- T. Kumar and S. Srivastava, "Resume Classification System using Natural Language Processing and Machine Learning Techniques," International Journal of Computational Intelligence and Information Security, vol. 5, no. 2, pp. 38-44, 2021.
- Lang Chain, "Lang Chain Documentation," accessed Oct. 3, 2024. [Online]. Available: https://python.langchain.com/
- MongoDB, Inc., "MongoDB Documentation," accessed Oct. 3, 2024. [Online]. Available: https://docs.mongodb.com/ AUTHORS
This study introduces the Next-Gen Talent
Matching System, an innovative JD-based CV filtering
web application designed to transform the recruitment
process by leveraging Large Language Models (LLMs)
and OpenAI technologies. Unlike traditional systems that
rely on skill-based c filtering, this system focuses on job
description (JD)-based filtering, providing greater
accuracy and relevance in candidate selection. By enabling
users to securely submit CVs, the system stores data in a
MongoDB database, allowing HR administrators to access
and match CVs based on semantic analysis. Using LLMs,
the system analyses job descriptions and CVs to rank
candidates according to how well they align with the job
requirements, taking into account skills, experience, and
qualifications. This approach enhances the efficiency of the
recruitment process by automating initial screening,
reducing human bias, and providing real-time feedback to
candidates. The Next-Gen Talent Matching System not
only improves the quality of candidate shortlisting but also
integrates with existing HR platforms and scales to handle
both small and large recruitment needs. Through its AI-
driven, data-centric approach, the system serves as a
powerful tool for modern recruitment, significantly
reducing the time and effort required by HR professionals
while ensuring more accurate and unbiased hiring
decisions.
Keywords :
JD-based Filtering, LLMs, OpenAI, AI-driven Recruitment, Semantic Analysis, Bias Reduction, Automated Candidate Matching.