Year: 2021
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Wang, Bojie, Lu, Yuheng
Institution: Beijing University of Posts and Telecommunications
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3201410197