计算机工程与应用2026，Vol.62Issue(5)：252-262,11.DOI:10.3778/j.issn.1002-8331.2501-0065
用于捆绑推荐的双视图对比学习
Dual-View Contrastive Learning for Bundle Recommendation
摘要
Abstract
Bundles can satisfy multiple user preferences at once.Most existing bundled recommendation models endeav-our to capture user preferences from different perspectives.However,these models encounter two problems:(1)The user preference for potential interaction bundles cannot be fully captured.(2)The correlations among bundles are not adequately extracted.To address these problems,the paper proposes a dual-view contrastive learning for bundle recommendation model(DCLBR).Specifically,DCLBR introduces an item-level hypergraph to capture the user preference for potential int-eraction bundles in item view,and an attention network is adopted to adaptively aggregate the representations of correlated items to obtain bundle representations.Then,this paper generates a bundle-level weighted graph to mine correlations among bundles in bundle view.In addition,in order to make the bundle more compatible with the preferences of users,the paper generates negative and positive bundles by performing data augmentation based on masking of important and unim-portant items,respectively.Contrastive learning is leveraged to make the final bundle representation adaptive to the impor-tance of the items.Extensive experiments on three public datasets show that this model outperforms baseline models.关键词
捆绑推荐/超图卷积网络(HGCN)/图卷积网络(GCN)/对比学习/双视图框架Key words
bundle recommendation/hypergraph convolutional network(HGCN)/graph convolutional network(GCN)/contrastive learning/dual-view framework分类
信息技术与安全科学引用本文复制引用
张尧,王绍卿,郑菁桦,韩小波,孙福振..用于捆绑推荐的双视图对比学习[J].计算机工程与应用,2026,62(5):252-262,11.基金项目
山东省自然科学基金(ZR2021MF017). （ZR2021MF017）
