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Leveraging Ontological Models in Recommender Systems for Skills Assessment and Personalized Career Development

In: Technological Innovations for Sustainable Development

Author

Listed:
  • Fatima Zahra Abbadi

    (Faculty of Science and Technology)

  • Mohamed Fourka

    (Faculty of Science and Technology)

  • Chahinaze Fikri Benbrahim

    (Faculty of Science and Technology)

Abstract

In the modern labor market, skill mismatch is one of the most significant challenges both employers and candidates face. Traditional recruitment and training systems often struggle to effectively match candidates' skills with job roles and the necessary training programs. Leveraging advanced technologies like recommender systems powered by ontological models presents a promising solution to this problem. This approach not only facilitates the matching of job roles with candidates but also supports personalized skill development paths by identifying gaps and recommending training or certification programs. This solution will also provide support for professionals to reorient themselves towards new professions. In this paper, we explore how an ontological model can be used within a recommender system for skills assessment and personalized job or training recommendations.

Suggested Citation

  • Fatima Zahra Abbadi & Mohamed Fourka & Chahinaze Fikri Benbrahim, 2025. "Leveraging Ontological Models in Recommender Systems for Skills Assessment and Personalized Career Development," Lecture Notes in Information Systems and Organization, in: Badr-Eddine Boudriki Semlali & Ikram Ben Abdel Ouahab & Fabio Angeletti (ed.), Technological Innovations for Sustainable Development, pages 394-403, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-06725-8_33
    DOI: 10.1007/978-3-032-06725-8_33
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