A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

TL;DR

This paper introduces a new machine learning-based method for automatically matching job offers with candidate profiles, aiming to improve recruitment efficiency and accuracy through learning from past cases.

Contribution

It presents a novel learning approach that leverages past matching cases to enhance prediction accuracy in job-candidate matching tasks.

Findings

The approach outperforms non-learning solutions significantly.

Empirical results demonstrate improved matching accuracy.

Learning from past cases enhances prediction reliability.

Abstract

Automatic matching of job offers and job candidates is a major problem for a number of organizations and job applicants that if it were successfully addressed could have a positive impact in many countries around the world. In this context, it is widely accepted that semi-automatic matching algorithms between job and candidate profiles would provide a vital technology for making the recruitment processes faster, more accurate and transparent. In this work, we present our research towards achieving a realistic matching approach for satisfactorily addressing this challenge. This novel approach relies on a matching learning solution aiming to learn from past solved cases in order to accurately predict the results in new situations. An empirical study shows us that our approach is able to beat solutions with no learning capabilities by a wide margin.