2021/09/30 by Junwei Zhang, Min Gao, Junliang Yu +3 · 1 citation
Computer Science · #Advanced Graph Neural Networks #Benchmark (surveying) #Expert finding and Q&A systems #Focus (optics) #Group (periodic table) #Heuristic #Hypergraph #Pairwise comparison #Preference #Recommender Systems and Techniques #Recommender system #cs.AI #cs.IR
paper · pdf · doi:10.1145/3459637.3482426
11 pages, 6 figures, CIKM 2021
openalex created_date 2021/09/13 · openalex publication_date 2021/10/26 · arxiv created 2022/03/20 · arxiv updated 2022/03/22 · openalex updated_date 2026/08/05
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.