Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively. However, they either use a unified view without differentiation or loosely combine the predictions of two separate views, while the crucial cooperative association between the two views' representations is overlooked. In this work, we propose to model the cooperative association between the two different views through cross-view contrastive learning. By encouraging the alignment of the two separately learned views, each view can distill complementary information from the other view, achieving mutual enhancement. Moreover, by enlarging the dispersion of different users/bundles, the self-discrimination of representations is enhanced. Extensive experiments on three public datasets demonstrate that our method outperforms SOTA baselines by a large margin. Meanwhile, our method requires minimal parameters of three set of embeddings (user, bundle, and item) and the computational costs are largely reduced due to more concise graph structure and graph learning module. In addition, various ablation and model studies demystify the working mechanism and justify our hypothesis. Codes and datasets are available at https://github.com/mysbupt/CrossCBR.
翻译:捆绑建议旨在向用户推荐一系列相关项目,这样可以一站式方便地满足用户的各种需要; 最近的方法通常利用用户-捆绑和用户-项目互动信息,为用户和捆绑分别提供与捆绑视图和项目视图相应的信息陈述; 然而,它们要么使用统一的观点,不加区别,要么粗略地将两种不同观点的预测合并起来,而这两种观点的表述之间至关重要的合作联系被忽视; 在这项工作中,我们建议通过交叉对比学习,模拟两种不同观点之间的合作联系;鼓励对两种分别学习的观点进行对齐,每一种观点都可以从其他观点中提取补充信息,实现相互加强; 此外,通过扩大不同用户/捆绑绑绑和捆绑,使代表的自我歧视得到加强; 对三个公共数据集的广泛实验表明,我们的方法大大超越了SOTA的基线; 同时,我们的方法要求三个嵌入(用户、捆绑和项目)的最小参数,而计算成本则由于更简明的图表结构和图表学习模块而大大降低; 各种标准/模型化研究是现有的模型/模型。