Graph Neural Netwrok with Interaction Pattern for Group Recommendation
This work addresses group recommendation for social platforms, presenting an incremental improvement by integrating interaction patterns into a graph neural network framework.
The paper tackles group recommendation by proposing GIP4GR, a graph neural network model that incorporates interaction patterns to learn group and item representations from user interaction histories, achieving superior performance on two real-world datasets.
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.