The group recommender system has become an important tool of social platforms to provide personalized and satisfied products or services for groups. However, existing methods of group recommendation mainly focus on improving the personalized recommendation methods, not only ignoring the interaction of users and groups, but also neglecting the dynamics of user preferences and group preferences. These interaction process and dynamic evolution are essential to group recommendation. Therefore, this paper proposes a dynamic group recommendation method based on the co-evolution of user preferences and group preferences to model the dynamic interaction between users and groups. Specifically, we model the user preferences as a weighted aggregation of user historical preferences and group influence, and model the group preferences as a weighted combination of group historical preferences and new members' preferences. Finally, we aim to predict users' joining behaviors and group consumption behaviors. We also carry out extensive experiments using real data to evaluate the effectiveness of our model. The experimental results show that the proposed model not only achieve better performances on predicting both joining and consumption behaviors, but also is robustness. © 2021, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
