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Due to the proliferation of social media, a growing number of users searchfor and join group activities in their daily life. This develops a need for thestudy on the ranking-based group identification (RGI) task, i.e., recommendinggroups to users. The major challenge in this task is how to effectively andefficiently leverage both the item interaction and group participation ofusers' online behaviors. Though recent developments of Graph Neural Networks(GNNs) succeed in simultaneously aggregating both social and user-iteminteraction, they however fail to comprehensively resolve this RGI task. Inthis paper, we propose a novel GNN-based framework named ContextualizedFactorized Attention for Group identification (CFAG). We devise tripartitegraph convolution layers to aggregate information from different types ofneighborhoods among users, groups, and items. To cope with the data sparsityissue, we devise a novel propagation augmentation (PA) layer, which is based onour proposed factorized attention mechanism. PA layers efficiently learn therelatedness of non-neighbor nodes to improve the information propagation tousers. Experimental results on three benchmark datasets verify the superiorityof CFAG. Additional detailed investigations are conducted to demonstrate theeffectiveness of the proposed framework.

Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph