Power users are not always powerful: The effect of social trust clusters in recommender systems

A recommender system (RS) is one that provides optimized information to users in an over-supply situation. The key to an RS is the accurate prediction of the behavior of the user. The matrix factorization (MF) method is used for this prediction in the early stages, and based on the recent development of social network service (SNS), social information is also utilized to improve the accuracy of prediction. In this paper, we use an RS internal trust cluster for the first time to further improve performance and analyze the characteristics of trust clusters. We propose a new approach, a trust-aware network (TAN) RS, to exploit these trust clusters. We also explore the impact and influence of power users in a social network-based RS using TAN RS, and analyze the impact of power users and clusters. From our experiments, we find that the use of TAN RS can enhance the prediction accuracy of RSs; we also show that power users and the sizes of clusters are not significant, and that normal users and ordinary sizes of clusters contribute to a reduction in prediction errors in social RSs.