A Design of Personalized Rating Method Including Herd Influence Using Spark-Hadoop Framework

Abstract

The bandwagon effect is a psychological phenomenon that other people's behaviors, attitudes produce an influence on a person. This phenomenon has been proved that it even has an effect to user behaviors in online marketing 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, I not only describe bandwagon effect and social group opinion as herd influence because they all can influence users' behaviors but also propose a novel formulation for predicting users' ratings by considering herd influence 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, I used Spark-Hadoop framework which can make operations with a high speed. As a consequence, the proposed method outperforms the existing model significantly in improving the prediction accuracy of users' ratings on RMSE.