A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling

Abstract

Recommender systems evaluate and filter the vast amount of information available on the Web, so they can be used to assist users in the process of accessing to relevant information. In the literature we can find countless approaches for generating personalized recommendations and all of them make use of different users’ and/or items’ features. In this sense, building accurate profiles plays an essential role in this context making the system’s success depend to a large extent on the ability of the learned profiles to represent the user’s preferences and needs. An ontology works very well to characterize the users profiles involved in the process of generating recommendations. In this paper we develop an ontology to characterize the trust between users using the fuzzy linguistic modeling, so that in the recommendation generation process we do not take into account users with similar ratings history but users in which each user can trust. We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and to update the user profiles based on the feedback.