Existing solutions for bundle recommendation (BR) have achieved remarkable effectiveness for predicting the user's preference for prebuilt bundles. However, bundle-item (B-I) affiliation will vary dynamically in real scenarios. For example, a bundle themed as 'casual outfit' may add 'hat' or remove 'watch' due to factors such as seasonal variations, changes in user preferences or inventory adjustments. Our empirical study demonstrates that the performance of mainstream BR models may fluctuate or decline under item-level variability. This paper makes the first attempt to address the above problem and proposes a novel Residual Diffusion for Bundle Recommendation(RDiffBR)asamodel-agnostic generative framework which can assist a BR model in adapting this scenario. During the initial training of the BR model, RDiffBR employs a residual diffusion model to process the item-level bundle embeddings which are generated by the BR model to represent bundle theme via a forward-reverse process. In the inference stage, RDiffBR reverses item-level bundle embeddings obtained by the well-trained bundle model under B-I variability scenarios to generate the effective item level bundle embeddings. In particular, the residual connection in our residual approximator significantly enhances BR models' ability to generate high-quality item-level bundle embeddings. Experiments on six BR models and four public datasets from different domains show that RDiffBR improves the performance of Recall and NDCG of backbone BR models by up to 23%, while only increases training time about 4%.

翻译：现有的捆绑推荐解决方案在预测用户对预构建捆绑包的偏好方面取得了显著成效。然而在实际场景中，捆绑包与物品的关联关系会动态变化。例如，以"休闲穿搭"为主题的捆绑包可能因季节变化、用户偏好调整或库存变动等因素而增加"帽子"或移除"手表"。我们的实证研究表明，主流捆绑推荐模型在项目级变化性下可能出现性能波动或下降。本文首次尝试解决上述问题，提出了一种新颖的残差扩散捆绑推荐框架——RDiffBR，该模型无关的生成框架能够辅助捆绑推荐模型适应此类场景。在捆绑推荐模型的初始训练阶段，RDiffBR采用残差扩散模型，通过前向-反向过程处理由捆绑推荐模型生成的、表征捆绑包主题的项目级捆绑嵌入。在推理阶段，RDiffBR对训练完备的捆绑模型在捆绑-物品关联变化场景下获得的项目级捆绑嵌入进行反向处理，从而生成有效的项目级捆绑嵌入。特别值得注意的是，我们设计的残差逼近器中的残差连接显著增强了捆绑推荐模型生成高质量项目级捆绑嵌入的能力。在六个捆绑推荐模型和四个不同领域的公开数据集上的实验表明，RDiffBR能将骨干捆绑推荐模型的召回率和NDCG指标提升最高达23%，而训练时间仅增加约4%。