文献类型：期刊文献

中文题名：基于双曲空间的多视图对比学习捆绑推荐模型

英文题名：Hyperbolic Space-Based Multi-View Contrastive Learning Model for Bundle Recommendation

作者：吴大卫[1];李建华[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2026

卷号：52

期号：1

起止页码：109

中文期刊名：华东理工大学学报(自然科学版)

外文期刊名：Journal of East China University of Science and Technology

收录：;北大核心:【北大核心2023】；

语种：中文

中文关键词：捆绑推荐;对比学习;双曲空间;图卷积网络;多视图融合

外文关键词：bundle recommendation;contrastive learning;hyperbolic space;graph convolutional network;multiview fusion

摘要：针对现有方法在捕捉交互图层次结构和多视图信息融合方面的不足,本文提出了一种基于双曲空间的多视图对比学习捆绑推荐(Hyperbolic Multi-view Contrastive learning for Bundle Recommendation,HMCBR)模型。该模型在3个视图的基础上,将实体嵌入双曲空间,并利用双曲图卷积网络学习各视图下的用户与捆绑包表示;同时,引入双曲自注意力机制自适应分配视图权重,以优化多视图信息融合;结合视图内对比学习和视图间对比学习,强化特征一致性与多视图信息交互。结果表明,HMCBR在3个主流的数据集上的表现均优于基线模型,能有效提升推荐效果。
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.