JobRecoGPT -- Explainable job recommendations using LLMs

TL;DR

This paper explores the use of Large Language Models to improve job recommendation systems by capturing nuanced unstructured data, comparing four different approaches for effectiveness and efficiency.

Contribution

It introduces a novel comparison of four job recommendation methods leveraging LLMs, highlighting their advantages and limitations in handling unstructured data.

Findings

LLM-guided approach performs well in capturing subtle job details.

Hybrid method balances accuracy and computational efficiency.

Traditional deterministic methods are faster but less nuanced.

Abstract

In today's rapidly evolving job market, finding the right opportunity can be a daunting challenge. With advancements in the field of AI, computers can now recommend suitable jobs to candidates. However, the task of recommending jobs is not same as recommending movies to viewers. Apart from must-have criteria, like skills and experience, there are many subtle aspects to a job which can decide if it is a good fit or not for a given candidate. Traditional approaches can capture the quantifiable aspects of jobs and candidates, but a substantial portion of the data that is present in unstructured form in the job descriptions and resumes is lost in the process of conversion to structured format. As of late, Large Language Models (LLMs) have taken over the AI field by storm with extraordinary performance in fields where text-based data is available. Inspired by the superior performance of LLMs, we leverage their capability to understand natural language for capturing the information that was previously getting lost during the conversion of unstructured data to structured form. To this end, we compare performance of four different approaches for job recommendations namely, (i) Content based deterministic, (ii) LLM guided, (iii) LLM unguided, and (iv) Hybrid. In this study, we present advantages and limitations of each method and evaluate their performance in terms of time requirements.