Leveraging Ontologies and Process Mining in Personalized Recruitment Recommendations - Smaili, Naoual; Lamghari, Zineb (Statistics, Optimization & Information Computing, 2025)
This paper presents a novel approach to improve recruitment methods through a comprehensive examination of contextual data and process models. The primary focus is on refining the process by aligning it with candidate preferences. The method incorporates ontology and process mining to provide contextual and sequential recommendations, adapting hunting methods according to candidate requests. Using a recruitment ontology and connecting it with candidate assessments, the approach refines strategies using successful recruitment historical data. Conformance checking identifies similar process models, connecting the ontology of each activity for a detailed analysis. The results highlight the effectiveness of the method in adjusting recruitment strategies based on historical and contextual data, offering a comprehensive and flexible solution for efficient recruitment.