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Article

Ontology-Based Personalized Job Recommendation Framework for Migrants and Refugees

by
Dimos Ntioudis
1,*,
Panagiota Masa
1,
Anastasios Karakostas
2,
Georgios Meditskos
1,3,
Stefanos Vrochidis
1 and
Ioannis Kompatsiaris
1
1
Centre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou—Thermi, 57001 Thessaloniki, Greece
2
Draxis Environmental, 54655 Thessaloniki, Greece
3
School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2022, 6(4), 120; https://doi.org/10.3390/bdcc6040120
Submission received: 29 July 2022 / Revised: 30 September 2022 / Accepted: 8 October 2022 / Published: 19 October 2022
(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)
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Abstract

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.
Keywords: recommendation systems; job matching; ontologies; reasoning; migrants; refugees recommendation systems; job matching; ontologies; reasoning; migrants; refugees

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MDPI and ACS Style

Ntioudis, D.; Masa, P.; Karakostas, A.; Meditskos, G.; Vrochidis, S.; Kompatsiaris, I. Ontology-Based Personalized Job Recommendation Framework for Migrants and Refugees. Big Data Cogn. Comput. 2022, 6, 120. https://doi.org/10.3390/bdcc6040120

AMA Style

Ntioudis D, Masa P, Karakostas A, Meditskos G, Vrochidis S, Kompatsiaris I. Ontology-Based Personalized Job Recommendation Framework for Migrants and Refugees. Big Data and Cognitive Computing. 2022; 6(4):120. https://doi.org/10.3390/bdcc6040120

Chicago/Turabian Style

Ntioudis, Dimos, Panagiota Masa, Anastasios Karakostas, Georgios Meditskos, Stefanos Vrochidis, and Ioannis Kompatsiaris. 2022. "Ontology-Based Personalized Job Recommendation Framework for Migrants and Refugees" Big Data and Cognitive Computing 6, no. 4: 120. https://doi.org/10.3390/bdcc6040120

APA Style

Ntioudis, D., Masa, P., Karakostas, A., Meditskos, G., Vrochidis, S., & Kompatsiaris, I. (2022). Ontology-Based Personalized Job Recommendation Framework for Migrants and Refugees. Big Data and Cognitive Computing, 6(4), 120. https://doi.org/10.3390/bdcc6040120

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