Improving scalability issues in collaborative filtering based on collaborative tagging using genre interestingness measure

Recommender Systems are non-profit websites to predict user preferences. In Commercial websites predicting accurate data may result higher selling rates. A recommender system compares user profiles to some reference characteristics, and seeks to predict the rating or preference that a user would give to an item that they have not yet considered. These characteristics may be considered as content-based approach, collaborative filtering demographic filtering and hybrid recommender systems. Collaborative filtering (CF) is widely used in recommender systems. These methods are based on collecting and analyzing the information of a particular user behavior, activity, preferences and will predict the user´s interest according to the similarity of other users. In this paper we address the problem of scalability associated with CF and propose a CF framework that combines collaborative tagging with genre interestingness measure for a movie RS. Our experiments on each movie dataset with recent timestamp demonstrate that the proposed CFT -GIM gives more accurate predictions of user´s ratings as compared to both CF and CFT.