Bundle recommendation aims to recommend a set of items to users for overall consumption. Existing bundle recommendation models primarily depend on observed user-bundle interactions, limiting exploration of newly-emerged bundles that are constantly created. It pose a critical representation challenge for current bundle methods, as they usually treat each bundle as an independent instance, while neglecting to fully leverage the user-item (UI) and bundle-item (BI) relations over popular items. To alleviate it, in this paper we propose a multi-view contrastive learning framework for cold-start bundle recommendation, named EpicCBR. Specifically, it precisely mine and utilize the item relations to construct user profiles, identifying users likely to engage with bundles. Additionally, a popularity-based method that characterizes the features of new bundles through historical bundle information and user preferences is proposed. To build a framework that demonstrates robustness in both cold-start and warm-start scenarios, a multi-view graph contrastive learning framework capable of integrating these diverse scenarios is introduced to ensure the model's generalization capability. Extensive experiments conducted on three popular benchmarks showed that EpicCBR outperforms state-of-the-art by a large margin (up to 387%), sufficiently demonstrating the superiority of the proposed method in cold-start scenario. The code and dataset can be found in the GitHub repository: https://github.com/alexlovecoding/EpicCBR.

翻译：捆绑推荐旨在向用户推荐一组物品以供整体消费。现有的捆绑推荐模型主要依赖于已观测到的用户-捆绑交互，这限制了对不断新创建的捆绑的探索。这对当前的捆绑推荐方法提出了关键的表示挑战，因为它们通常将每个捆绑视为独立实例，而未能充分利用热门物品上的用户-物品（UI）和捆绑-物品（BI）关系。为缓解此问题，本文提出一种面向冷启动捆绑推荐的多视图对比学习框架，命名为EpicCBR。具体而言，该框架精准挖掘并利用物品关系构建用户画像，以识别可能对捆绑感兴趣的用户。此外，提出一种基于流行度的方法，通过历史捆绑信息和用户偏好来刻画新捆绑的特征。为构建在冷启动和热启动场景下均表现鲁棒的框架，引入了一种能够整合这些多样化场景的多视图图对比学习框架，以确保模型的泛化能力。在三个主流基准数据集上进行的大量实验表明，EpicCBR以显著优势（最高达387%）超越现有最优方法，充分证明了所提方法在冷启动场景中的优越性。代码与数据集可在GitHub仓库获取：https://github.com/alexlovecoding/EpicCBR。