  With the development of social platforms, people are more and more inclined
to combine into groups to participate in some activities, so group
recommendation has gradually become a problem worthy of research. For group
recommendation, an important issue is how to obtain the characteristic
representation of the group and the item through personal interaction history,
and obtain the group's preference for the item. For this problem, we proposed
the model GIP4GR (Graph Neural Network with Interaction Pattern For Group
Recommendation). Specifically, our model use the graph neural network framework
with powerful representation capabilities to represent the interaction between
group-user-items in the topological structure of the graph, and at the same
time, analyze the interaction pattern of the graph to adjust the feature output
of the graph neural network, the feature representations of groups, and items
are obtained to calculate the group's preference for items. We conducted a lot
of experiments on two real-world datasets to illustrate the superior
performance of our model.
