Bundle recommendation aims to suggest a set of interconnected items to users. However, diverse interaction types and sparse interaction matrices often pose challenges for previous approaches in accurately predicting user-bundle adoptions. Inspired by the distant supervision strategy and generative paradigm, we propose BRIDGE, a novel framework for bundle recommendation. It consists of two main components namely the correlation-based item clustering and the pseudo bundle generation modules. Inspired by the distant supervision approach, the former is to generate more auxiliary information, e.g., instructive item clusters, for training without using external data. This information is subsequently aggregated with collaborative signals from user historical interactions to create pseudo `ideal' bundles. This capability allows BRIDGE to explore all aspects of bundles, rather than being limited to existing real-world bundles. It effectively bridging the gap between user imagination and predefined bundles, hence improving the bundle recommendation performance. Experimental results validate the superiority of our models over state-of-the-art ranking-based methods across five benchmark datasets.

翻译：捆绑推荐旨在向用户推荐一组相互关联的物品。然而，多样化的交互类型和稀疏的交互矩阵往往给现有方法在准确预测用户-捆绑采纳行为时带来挑战。受远监督策略和生成范式的启发，我们提出了BRIDGE，一种新颖的捆绑推荐框架。该框架主要由两个核心组件构成：基于相关性的物品聚类模块和伪捆绑生成模块。受远监督方法启发，前者旨在不依赖外部数据的情况下生成更多辅助信息（例如指导性物品聚类）用于训练。这些信息随后与用户历史交互中的协同信号进行聚合，以创建伪“理想”捆绑。这一能力使得BRIDGE能够探索捆绑的所有可能方面，而不仅限于现实世界中已存在的捆绑。它有效弥合了用户想象与预定义捆绑之间的差距，从而提升了捆绑推荐的性能。实验结果在五个基准数据集上验证了我们的模型相较于最先进的基于排序方法的优越性。