Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

TL;DR

This paper introduces a novel self-supervised hypergraph learning framework for group recommendation that captures complex user interactions and mitigates data sparsity through a hierarchical hypergraph and double-scale node dropout.

Contribution

It proposes a double-scale self-supervised hypergraph model that models high-order user interactions and alleviates data sparsity in group recommendation.

Findings

Outperforms existing methods on benchmark datasets.

Effectively models complex intra- and inter-group user interactions.

Reduces data sparsity issues through self-supervision.

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.