Método híbrido de recomendación adaptativa de objetos de aprendizaje basado en perfiles de usuario

Abstract

The aim of this paper is to propose a hybrid method for adaptive recommendation of learning objects that initially performs a selection of characteristics from the student profile that best define it. Hybrid recommendation systems of digital educational materials support virtual learning processes and help students finding relevant resources that are adapted to their needs and preferences. Then, the recommendation algorithms are selected and applied, followed by the choice of how to combine the results of these techniques. Finally, the integration of techniques is evaluated considering the relevance obtained by applying the recommendation techniques. It can be concluded that using adaptive hybrid recommendation systems, improves the generation of relevant results, especially when the combination should include the knowledge-based filtering technique.