Graph Neural Netwrok with Interaction Pattern for Group Recommendation

TL;DR

This paper introduces GIP4GR, a graph neural network model that captures interaction patterns in social group data to improve group recommendation accuracy.

Contribution

The paper proposes a novel GNN model that incorporates interaction pattern analysis for enhanced group and item feature representation in recommendations.

Findings

GIP4GR outperforms existing models on real-world datasets.

Interaction pattern analysis improves recommendation precision.

The model effectively captures complex group-user-item interactions.

Abstract

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.