Modeling user preferences in a hybrid recommender system using type-2 fuzzy sets

Recommender Systems are a class of applications which are used to overcome the problem of information overload. They use the opinions of members of a community to help individuals in that community identify the information most likely to be interesting to them or relevant to their needs, by drawing on user preferences and filtering the set of possible options to a more manageable subset. The key element of such user-support systems is the user model. Traditional techniques used to create user models are usually too rigid to capture the inherent uncertainty of human behavior. Fuzzy sets can handle and process uncertainty in human decision-making and if used in user modeling can be of advantage as it will result in recommendations closely meeting user preferences. In this paper, a hybrid multi-agent recommender system is designed and developed where user´s preferences; needs and satisfaction are modeled using interval type-2 (IT2) fuzzy sets. This results in improving the prediction accuracy of the system and hence better recommendations are generated. Experimental study was conducted on book recommender system and promising results were obtained.