A novel approach for learning how to automatically match job offers and candidate profiles

Autoren Jorge Martínez Gil
Lorena Paoletti
Mario Pichler
Editoren
Titel A novel approach for learning how to automatically match job offers and candidate profiles
Typ sonst
Verlag cs.OH - Other Computer Science, arXiv:1611.04931 [cs.OH]
Serie https://arxiv.org/abs/1611.04931
Monat November
Jahr 2016
SCCH ID# 16103
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.