# Dynamic Group Recommendation Based on the Attention Mechanism
## 1 Introduction  
With the rapid development of information technology, the recommendation system has become an important tool for people to solve information overload issues. The recommendation system has been widely used in many online information systems, such as social media sites, E-commerce platforms, etc., to help users choose products [1; 2; 3] that meet their needs. In addition, the recommendation system is also widely used in medical diagnosis, for example, similar patients may be recommended for similar treatment in one group [4].  
Now most of the recommendation systems are designed for individual users, however, group activities have become more and more popular, such as, watching a movie with friends, and planning a trip with other travelers, and having dinner with spouses. Therefore, this requires recommendations for a group of users. Group recommendation [5; 6; 7] is different from individual recommendation. In a group recommendation, it is used not only to consider the preferences of individual users, but also to consider the preferences of users in the whole group, trying to meet the individual preferences of all members in the group, as much as possible. This requires considering the influence of each member in the group and balancing the preference differences among users. The process is complex and dynamic. For example, a member may play different roles and influences in deciding different types of items due to his/her different expertise. Most of the existing group recommendation methods based on memory and a model adopt predefined group recommendation strategies, which cannot make group decisions dynamically. As a result, the recommendation effect is not ideal.  
As a premise of group recommendation, we ask how to discover the potential group, and how to aggregate the preferences of the members of the group to get the results of group recommendation.  
Specifically, in this work, we propose an improved density peak clustering method to realize group discovery, and use the neural attention network to explore the weight of each user in the group recommendation. By comparing random groups that take the family as the unit, we conclude that the groups with high similarity can be obtained by clustering, and the higher the similarity, the better the group recommendation effect is.  
The main work of this paper is summarized as follows:  
* This paper proposes a method for constructing potential groups based on the density peak clustering algorithm. The original density peak clustering algorithm is improved to construct highly similar user sets and realize group division. Improve group recommendation performance.
* Construct the AMGR (attention mechanism group recommended) model and use the neural attention network to dynamically fuse the weight of user preferences within the group to implement group recommendation.
* We have conducted experiments on a public data set. The experimental results show that the attention network can dynamically capture the overall decision-making process of the group, and the more alike the users in the group are, the better the group recommendation effect will be.