Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology

Carlos Porcel, Julio Herce-Zelaya, Juan Bernabé-Moreno, Álvaro Tejeda-Lorente, Enrique Herrera-Viedma

Abstract


The rapid advances in Web technologies are promoting the development of new pedagogic models based on virtual teaching. In this framework, personalized services are necessary. Recommender systems can be used in an academic environment to assist users in their teaching-learning processes. In this paper, we present a trust based recommender system, adopting a fuzzy linguistic modeling, that provides personalized activities to students in order to reinforce their education, and applied it in the field of oral surgery and implantology. We don’t take into account users with similar ratings history but users in which each user can trust and we provide a method to aggregate the trust information. This system can be used in order to aid professors to provide students with a personalized monitoring of their studies with less effort. The results obtained in the experiments proved to be satisfactory.

Keywords


recommender system, e-learning, fuzzy linguistic modeling, oral surgery

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References


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DOI: https://doi.org/10.15837/ijccc.2020.3.3858



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