Prediction of personalized rating by combining bandwagon effect and social group opinion: Using Hadoop-Spark framework

The bandwagon effect is a psychological phenomenon that a person’s behaviors, attitudes, and beliefs are influenced by other people [1]. This phenomenon has been proved that it has an influence to user behaviors, even in online environment. However, a few studies have considered both of the bandwagon effect and social group opinion simultaneously for improving personalized rating prediction performance. In this paper, we propose a novel formulation for the users’ ratings that each rating is considered as a function of user preference rating and group-based social opinion which are adjusted by bandwagon effect. For to process real big data, we used Hadoop-Spark framework which can make high-speed operations. As a result, our method outperforms the existing model significantly in improving the prediction accuracy of users’ ratings on RMSE.