Next-Gen Talent Matching System: Innovating Recruitment with AI-Driven JD and CV Matching


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.

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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.

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