Ontology-based personalized job recommendation framework for migrants and refugees
Ntioudis, Dimos (Centre for Research & Technology Hellas)
Masa, Panagiota (Centre for Research & Technology Hellas)
Karakostas, Anastasios (Draxis Environmental)
Meditskos, Georgios (Centre for Research & Technology Hellas)
Vrochidis, Stefanos (Centre for Research & Technology Hellas)
Kompatsiaris, Ioannis (Centre for Research & Technology Hellas)

Data:	2022
Resum:	Participation in the labor market is seen as the most important factor favoring long-term integration of migrants and refugees into society. This paper describes the job recommendation framework of the Integration of Migrants MatchER SErvice (IMMERSE). The proposed framework acts as a matching tool that enables the contexts of individual migrants and refugees, including their expectations, languages, educational background, previous job experience and skills, to be captured in the ontology and facilitate their matching with the job opportunities available in their host country. Profile information and job listings are processed in real time in the back-end, and matches are revealed in the front-end. Moreover, the matching tool considers the activity of the users on the platform to provide recommendations based on the similarity among existing jobs that they already showed interest in and new jobs posted on the platform. Finally, the framework takes into account the location of the users to rank the results and only shows the most relevant location-based recommendations.
Ajuts:	European Commission 870939
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Recommendation systems ; Job matching ; Ontologies ; Reasoning ; Migrants ; Refugees
Publicat a:	Big Data and Cognitive Computing, Vol. 6, núm. 4 (2022) , ISSN 2504-2289

DOI: 10.3390/bdcc6040120

24 p, 1.3 MB

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