Group Recommendation Based on the PageRank

Abstract

Social network greatly improve the social recommendation application, especially the study of group recommendation. The group recommendation, analyze the social factors of the group, such as social and trust relationship between users, as the factors for the prediction model established. In this paper, PageRank algorithm is introduced in the recommendation method to calculate the member’s importance in the group respectively, and to amend the aggregate function of individual preferences. The aggregate function consider the relationship between various users in the group, and optimize the aggregate function according to users different influence on the group, which can better reflect the social characteristics of group. In short, the study on group recommended model and algorithm can take the initiative to find the user's needs. Extensive experiments demonstrate the effectiveness and efficiency of the methods, which improve the prediction accuracy of the group recommended algorithms.