  Due to the proliferation of social media, a growing number of users search
for and join group activities in their daily life. This develops a need for the
study on the ranking-based group identification (RGI) task, i.e., recommending
groups to users. The major challenge in this task is how to effectively and
efficiently leverage both the item interaction and group participation of
users' online behaviors. Though recent developments of Graph Neural Networks
(GNNs) succeed in simultaneously aggregating both social and user-item
interaction, they however fail to comprehensively resolve this RGI task. In
this paper, we propose a novel GNN-based framework named Contextualized
Factorized Attention for Group identification (CFAG). We devise tripartite
graph convolution layers to aggregate information from different types of
neighborhoods among users, groups, and items. To cope with the data sparsity
issue, we devise a novel propagation augmentation (PA) layer, which is based on
our proposed factorized attention mechanism. PA layers efficiently learn the
relatedness of non-neighbor nodes to improve the information propagation to
users. Experimental results on three benchmark datasets verify the superiority
of CFAG. Additional detailed investigations are conducted to demonstrate the
effectiveness of the proposed framework.
