Double-Scale Self-Supervised Hypergraph Learning for Group
Recommendation
- URL: http://arxiv.org/abs/2109.04200v1
- Date: Thu, 9 Sep 2021 12:19:49 GMT
- Title: Double-Scale Self-Supervised Hypergraph Learning for Group
Recommendation
- Authors: Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, Hongzhi Yin
- Abstract summary: Group recommendation suffers seriously from the problem of data sparsity.
We propose a self-supervised hypergraph learning framework for group recommendation to achieve two goals.
- Score: 35.841350982832545
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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