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

Jorge Martinez-Gil (), Alejandra Lorena Paoletti and Mario Pichler
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Jorge Martinez-Gil: Software Competence Center Hagenberg GmbH
Alejandra Lorena Paoletti: Software Competence Center Hagenberg GmbH
Mario Pichler: Software Competence Center Hagenberg GmbH

Information Systems Frontiers, 2020, vol. 22, issue 6, No 1, 1265-1274

Abstract: 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.

Keywords: Human resources management systems; Knowledge engineering; e-Recruitment (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10796-019-09929-7

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