With the prevalence of social media, there has recently been a proliferation
of recommenders that shift their focus from individual modeling to group
recommendation. Since the group preference is a mixture of various
predilections from group members, the fundamental challenge of group
recommendation is to model the correlations among members. Existing methods
mostly adopt heuristic or attention-based preference aggregation strategies to
synthesize group preferences. However, these models mainly focus on the
pairwise connections of users and ignore the complex high-order interactions
within and beyond groups. Besides, group recommendation suffers seriously from
the problem of data sparsity due to severely sparse group-item interactions. In
this paper, we propose a self-supervised hypergraph learning framework for
group recommendation to achieve two goals: (1) capturing the intra- and
inter-group interactions among users; (2) alleviating the data sparsity issue
with the raw data itself. Technically, for (1), a hierarchical hypergraph
convolutional network based on the user- and group-level hypergraphs is
developed to model the complex tuplewise correlations among users within and
beyond groups. For (2), we design a double-scale node dropout strategy to
create self-supervision signals that can regularize user representations with
different granularities against the sparsity issue. The experimental analysis
on multiple benchmark datasets demonstrates the superiority of the proposed
model and also elucidates the rationality of the hypergraph modeling and the
double-scale self-supervision.

本文提出了一个自我监督超图学习框架，用于解决群体推荐中用户之间的复杂关联模型问题。实验分析表明，该模型在多个基准数据集上的表现优于现有方法，并阐明了超图建模和双尺度自我监督的合理性。