华东理工大学学报（自然科学版）2026，Vol.52Issue(1)：109-117,9.DOI:10.14135/j.cnki.1006-3080.20250326002
基于双曲空间的多视图对比学习捆绑推荐模型
Hyperbolic Space-Based Multi-View Contrastive Learning Model for Bundle Recommendation
吴大卫 1李建华1
作者信息
- 1. 华东理工大学信息科学与工程学院,上海 200237
- 折叠
摘要
Abstract
Bundle recommendation aims at recommending a set of related items(bundles)to users.To address the limitations of existing methods in capturing the hierarchical structure of interaction graphs and integrating multi-view information,this paper proposes a hyperbolic multi-view contrastive learning for bundle recommendation(HMCBR)model.The model embeds entities into hyperbolic space and leverages a hyperbolic graph convolutional network to learn user and bundle representations across different views.Additionally,a hyperbolic self-attention mechanism is introduced to adaptively allocate view weights,optimizing multi-view information fusion.Moreover,both intra-view and inter-view contrastive learning are incorporated to enhance feature consistency and multi-view information interaction.Experimental results demonstrate that HMCBR outperforms baseline models on three benchmark datasets,effectively improving recommendation performance.关键词
捆绑推荐/对比学习/双曲空间/图卷积网络/多视图融合Key words
bundle recommendation/contrastive learning/hyperbolic space/graph convolutional network/multi-view fusion分类
信息技术与安全科学引用本文复制引用
吴大卫,李建华..基于双曲空间的多视图对比学习捆绑推荐模型[J].华东理工大学学报（自然科学版）,2026,52(1):109-117,9.
