基于双曲空间的多视图对比学习捆绑推荐模型OACHSSCD

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.