用生成式方法构建虚拟商品包，提升推荐精准度
BRIDGE: Bundle Recommendation via Instruction-Driven Generation
- 基于物品关联聚类生成辅助指令信息
- 融合历史行为生成伪理想商品包，提升推荐效果
- 适合研究推荐系统与生成模型交叉的学者
捆绑推荐旨在向用户推荐一组相互关联的商品。然而，多样的交互类型和稀疏的交互矩阵使得以往方法难以准确预测用户对商品包的采纳。受远程监督策略和生成范式启发，我们提出BRIDGE框架，包含基于相关性的物品聚类模块和伪商品包生成模块。前者通过生成指导性物品聚类等辅助信息进行训练，无需外部数据；这些信息与用户历史交互的协同信号结合，生成伪‘理想’商品包。该能力使模型可探索商品包的全部潜在组合，而非局限于现有真实商品包，有效弥合用户想象与预定义商品包之间的差距，从而提升推荐性能。实验结果表明，我们的模型在五个基准数据集上均优于现有基于排名的方法。
原文摘要 · Abstract (English)
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.
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