In recent years, bundle recommendation systems have gained significant attention in both academia and industry due to their ability to enhance user experience and increase sales by recommending a set of items as a bundle rather than individual items. This survey provides a comprehensive review on bundle recommendation, beginning by a taxonomy for exploring product bundling. We classify it into two categories based on bundling strategy from various application domains, i.e., discriminative and generative bundle recommendation. Then we formulate the corresponding tasks of the two categories and systematically review their methods: 1) representation learning from bundle and item levels and interaction modeling for discriminative bundle recommendation; 2) representation learning from item level and bundle generation for generative bundle recommendation. Subsequently, we survey the resources of bundle recommendation including datasets and evaluation metrics, and conduct reproducibility experiments on mainstream models. Lastly, we discuss the main challenges and highlight the promising future directions in the field of bundle recommendation, aiming to serve as a useful resource for researchers and practitioners. Our code and datasets are publicly available at https://github.com/WUT-IDEA/bundle-recommendation-survey.

翻译：近年来，捆绑推荐系统因其能够通过推荐一组商品（而非单个商品）来提升用户体验并增加销售额，在学术界和工业界获得了广泛关注。本综述对捆绑推荐进行了全面回顾，首先通过产品捆绑的分类法展开探讨。我们根据不同应用领域的捆绑策略将其分为两类，即判别式捆绑推荐与生成式捆绑推荐。随后，我们对这两类任务进行了形式化定义，并系统性地回顾了其方法：1）针对判别式捆绑推荐的捆绑与商品层级表征学习及交互建模；2）针对生成式捆绑推荐的商品层级表征学习与捆绑生成。接着，我们综述了捆绑推荐的资源，包括数据集与评估指标，并对主流模型进行了可复现性实验。最后，我们探讨了该领域的主要挑战，并指出了未来具有前景的研究方向，旨在为研究人员和实践者提供有价值的参考资源。我们的代码与数据集已在 https://github.com/WUT-IDEA/bundle-recommendation-survey 公开。