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

Author

Listed:
  • Jorge Martinez-Gil

    (Software Competence Center Hagenberg GmbH)

  • Alejandra Lorena Paoletti

    (Software Competence Center Hagenberg GmbH)

  • Mario Pichler

    (Software Competence Center Hagenberg GmbH)

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.

Suggested Citation

  • Jorge Martinez-Gil & Alejandra Lorena Paoletti & Mario Pichler, 2020. "A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles," Information Systems Frontiers, Springer, vol. 22(6), pages 1265-1274, December.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:6:d:10.1007_s10796-019-09929-7
    DOI: 10.1007/s10796-019-09929-7
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    References listed on IDEAS

    as
    1. Jorge Martinez-Gil, 2014. "An Overview of Knowledge Management Techniques for e-Recruitment," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-9.
    2. Jorge Martinez-Gil & José F. Aldana-Montes, 2013. "Semantic similarity measurement using historical google search patterns," Information Systems Frontiers, Springer, vol. 15(3), pages 399-410, July.
    Full references (including those not matched with items on IDEAS)

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