Published Paper Details:

The drawbacks of traditional job recommendation systems built upon static keyword matching have created a need for more intelligent and adaptive recruitment processes. In this paper, we introduce a novel AI job recommendation framework utilizing Large Language Models (LLMs) to summarize resumes and identify skills gaps. Our method allows the automatic identification of candidate information and dynamically determines the fit between a candidate's resume and the skills needed for a job role by analyzing the user's own skills and the job requirements. Further, we are able to provide quick and transparent recommendations with vector similarity search technology-backed databases. The well over half of respondents in our study expressed excitement about summarizing resumes and analyzing skills using AI, demonstrating the potential of this system to assist in recruitment by providing easy-to-understand, timely, and personalized recommendations, without the need for human intervention.