Stochastic Deep Graph Clustering for Practical Group Formation
This addresses the challenge of forming and adjusting groups in real-time for group recommender systems, which is incremental over prior static approaches.
The paper tackles the problem of dynamic group formation in group recommender systems by proposing DeepForm, a framework that achieves superior group formation quality, efficiency, and recommendation accuracy compared to baselines.
While prior work on group recommender systems (GRSs) has primarily focused on improving recommendation accuracy, most approaches assume static or predefined groups, making them unsuitable for dynamic, real-world scenarios. We reframe group formation as a core challenge in GRSs and propose DeepForm (Stochastic Deep Graph Clustering for Practical Group Formation), a framework designed to meet three key operational requirements: (1) the incorporation of high-order user information, (2) real-time group formation, and (3) dynamic adjustment of the number of groups. DeepForm employs a lightweight GCN architecture that effectively captures high-order structural signals. Stochastic cluster learning enables adaptive group reconfiguration without retraining, while contrastive learning refines groups under dynamic conditions. Experiments on multiple datasets demonstrate that DeepForm achieves superior group formation quality, efficiency, and recommendation accuracy compared with various baselines.