动态分组新框架，实时生成高质量用户群组。
Stochastic Deep Graph Clustering for Practical Group Formation
- 用轻量GCN捕捉用户高阶关系，构建动态图结构。
- 无需重训练即可自适应调整群组数量与成员。
- 适合需要实时分组的推荐系统场景。
现有群组推荐系统多关注推荐准确率，但普遍假设群组静态或预先设定，难以应对真实世界的动态场景。本文将群组形成重新定义为群组推荐的核心挑战，提出DeepForm（随机深度图聚类用于实际群组形成）框架，满足三项关键操作需求：(1) 融合高阶用户信息，(2) 实时群组生成，(3) 动态调整群组数量。DeepForm采用轻量级GCN架构，有效捕获高阶结构信号；随机聚类学习实现无需重训练的自适应群组重构；对比学习在动态条件下优化群组质量。在多个数据集上的实验表明，DeepForm在群组形成质量、效率和推荐准确率上均优于多种基线方法。
原文摘要 · Abstract (English)
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.
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