Diversified Group Recommendation Model for Social Network

Dong Li, Zhenshuo Liu, Zhanghui Wang, Jin Liu, Yue Kou, Lingling Zhang

Abstract: Group recommendation can recommend satisfactory activities to group members in the recommendation system. In the research of group recommendation, the main issue is how to combine the preferences of different group members. Most of the existing group recommendations adopt a single aggregation strategy to aggregate the preferences of different group members, unable to fulfill the needs of diversified group decision-making. At the same time, most of these group recommendation methods rely on intuition or hypothesis to analyze the influence of group members, which lacks convincing theoretical support. To overcome this issue, we propose the Diversified Group Recommendation Model for social network (DGRM). This model considers two aspects of social choice and social influence, models the diversity of groups, and adopts different group recommendation strategies for different groups, which can better meet the diverse needs of users and groups. We propose a group recommendation strategy based on score fusion, which can better meet the diverse needs of users and groups. Firstly, a matrix factorization-based individual rating prediction method and a Bayesian model-based individual rating prediction method are proposed, respectively, to predict the individual ratings based on user-item interactions. Secondly, different strategies for scoring fusion are proposed, and group recommendations are made based on the fused scores for different types of groups. Finally, we verify the feasibility and effectiveness of the key technologies proposed in this paper by conducting experiments, which demonstrates the effectiveness of our proposed methods.