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

摘要

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.