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A Novel Personalized Preference-based Approach for Job/Candidate Recommendation

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Research Challenges in Information Science (RCIS 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 415))

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Abstract

Although fuzzy-based recommendation systems are widely used in several services, scanty efforts have been carried out to investigate the efficiency of such approaches in job recommendation applications. In fact, most of the existing fuzzy-based job recommendation systems are only considering two crisp criteria: Curriculum Vitae (CV) content and job description. Other factors like personalized users needs and the fuzzy nature of their explicit and implicit preferences are totally ignored. To fill this gap, this paper introduces a new fuzzy personalized job recommendation approach aiming at providing a more accurate and selective job/candidate matching. To this end, our contribution considers a Fuzzy NoSQL Preference Model to define the candidates profiles. Based on this modeling, an efficient Fuzzy Matching/Scoring algorithm is then applied to select the top-k personalized results. The proposed framework has been added as an extension to TeamBuilder software. Through extensive experimentations using real data sets, achieved results corroborate the efficiency of our approach in providing accurate and personalized results.

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Authors and Affiliations

  1. Umanis - Research & Innovation, Levallois Perret, France

    Olfa Slama & Patrice Darmon

Authors
  1. Olfa Slama
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  2. Patrice Darmon
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Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Samira Cherfi

  2. Fondazione Bruno Kessler, Trento, Italy

    Anna Perini

  3. University Paris 1 Panthéon-Sorbonne, Paris, France

    Selmin Nurcan

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Slama, O., Darmon, P. (2021). A Novel Personalized Preference-based Approach for Job/Candidate Recommendation. In: Cherfi, S., Perini, A., Nurcan, S. (eds) Research Challenges in Information Science. RCIS 2021. Lecture Notes in Business Information Processing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-030-75018-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-75018-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75017-6

  • Online ISBN: 978-3-030-75018-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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