# Dynamic Group Recommendation Based on the Attention Mechanism
## 2 Related Work
### Group Recommended  
In recent years, the recommendation system has been widely applied to various industries, such as social, e-commerce, medical diagnosis and many studies have been conducted on individual recommendation technology, but research on group recommendation is limited. At present, group recommendation methods can be divided into two categories: Memory-based methods and model-based methods [22; 23].  
The memory-based methods in group recommendation include the preference aggregation method [24] and score aggregation method [25]. The preference aggregation method is to aggregate the profiles of users in all groups into a new profile, and then perform group recommendations for the user. The score aggregation method calculates the score of each candidate item for each user in the group, and then aggregates the score through a predefined strategy to represent the group's predictive preference.  
Common predefined strategies include the average strategy (AVG) [26], the least misery strategy (LM) [27], most pleasure strategy (MP) [28], etc. AVG first calculates the preference score of each user in the group for each item, and takes the average score as the group preference score. LM selects the lowest of all items scores as the group item recommendation final score. MP selects the highest of all items scores as the final score of group item recommendation. However, these methods are not flexible.  
The model-based method is different from the memory-based method. The model-based method first uses the interaction between users in the group to model according to preference information, and then aggregates to generate the preference model of the group. For example, in the personal impact topic model (PIT) [29], select the most influential user by considering the influence of the group members, whose decision should represent the group decision. However, this is not the case. Only when the most influential user is an expert in the relevant field, his decision-making is helpful for group recommendation. For example, a food expert may decide which restaurant a group of people go to eat, but may not be the person who decides which movie to go to the theater. The consensus model (COM) [30] proposes that the influence of the user depends on the group decision-making theme, and the decision-making process is influenced by two aspects: The group preference topic and the user's personal preference. The downside of the model at this point is that users cannot dynamically make decisions across groups. In addition, there is a recommendation algorithm based on matrix factorization (MF) [31] methods. MF is the decomposition of a matrix into the product of two or more matrices, in which the most basic data is the user-item scoring matrix, and the missing score can be converted into a regression problem based on machine learning. The advantage of this method is that the prediction accuracy is high, but the model training is time-consuming and does not have a good interpretation.  
In recent years, deep learning has been widely applied to the recommendation system by the research community, which combines the deep learning method with the recommendation system to capture various relationships between users and item. The literature [32; 33] has a comprehensive review. However, there are relatively few researches on group recommendation using deep learning. In this paper, we used the neural network and attention mechanism to dynamically learn the weights of users in the group and make group recommendations. The specific details will be shown below.