Sharing notes: An academic social network based on a personalized fuzzy linguistic recommender system

Social networks are Web systems that enable and encourage a collaborative work, making it possible to exchange information between users, which makes them especially useful in many areas. Specifically, they could be used in an academic environment with the aim of improving the educational processes, not replacing, but complementing the most traditional face-to-face models. But nowadays the increasingly widespread use of new technologies and social networks is causing the information we have available to grow disproportionately, making it more difficult and expensive to access information of interest. To alleviate this problem, automatic tools such as recommender systems, could be used to facilitate the accesses to relevant information, that in an academic environment would help to customize the educational processes. So, in this paper we present SharingNotes, an academic social network that can generate personalized recommendations to improve teaching and learning processes. To achieve this goal, it incorporates a hybrid recommender system that uses an ontology to characterize the degrees of trust among network users, and adopts the fuzzy linguistic modeling to improve the representation of information. Then, the use of this platform allows adapting the educational process to the circumstances of each student. The evaluation developed demonstrates the usefulness of this educational social network, as well as the users' satisfaction while interacting and working with it.