Efficient recommendation for smart TV contents

Abstract

In this paper, we propose an efficient recommendation technique for smart TV contents. Our method solves the scalability and sparsity problems from which the conventional algorithms suffer in smart TV environment characterized by the large numbers of users and contents. Our method clusters users into user groups of similar preference patterns and a set of similar users to the target user are extracted, and then the user-based collaborative filtering is applied. We experimented with our method using the data of the real one-month IPTV services. The experiment results showed the success rate of 93.6% and the precision of 77.4%, which are recognized as a good performance for smart TV. We also investigate integration of recommendation methods for more personalized and efficient recommendation. Category match ratios for different integrations are compared as a measure for personalized recommendation. © Springer-Verlag 2012.