Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology Porcel Gallego, Carlos Gustavo Herce Zelaya, Julio Bernabé Moreno, Juan Tejeda Lorente, Álvaro Herrera Viedma, Enrique Recommender system E-learning Fuzzy linguistic modeling Oral surgery 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. 2020-06-01T10:59:31Z 2020-06-01T10:59:31Z 2020-06 journal article Porcel, C.; Herce-Zelaya, J.; Bernabé-Moreno, J.; Tejeda-Lorente, A.; Herrera-Viedma, E. (2020). Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology, International Journal of Computers Communications & Control, 15(3), 3858, 2020. [https://doi.org/10.15837/ijccc.2020.3.3858] http://hdl.handle.net/10481/62309 10.15837/ijccc.2020.3.3858 eng http://creativecommons.org/licenses/by-nc/3.0/es/ open access Atribución-NoComercial 3.0 España Agora University of Oradea