The growth of e-commerce has seen a surge in popularity of platforms like Amazon, eBay, and Taobao. This has given rise to a unique shopping behavior involving baskets - sets of items purchased together. As a less studied interaction mode in the community, the question of how should shopping basket complement personalized recommendation systems remains under-explored. While previous attempts focused on jointly modeling user purchases and baskets, the distinct semantic nature of these elements can introduce noise when directly integrated. This noise negatively impacts the model's performance, further exacerbated by significant noise (e.g., a user is misled to click an item or recognizes it as uninteresting after consuming it) within both user and basket behaviors. In order to cope with the above difficulties, we propose a novel Basket recommendation framework via Noise-tolerated Contrastive Learning, named BNCL, to handle the noise existing in the cross-behavior integration and within-behavior modeling. First, we represent the basket-item interactions as the hypergraph to model the complex basket behavior, where all items appearing in the same basket are treated as a single hyperedge. Second, cross-behavior contrastive learning is designed to suppress the noise during the fusion of diverse behaviors. Next, to further inhibit the within-behavior noise of the user and basket interactions, we propose to exploit invariant properties of the recommenders w.r.t augmentations through within-behavior contrastive learning. A novel consistency-aware augmentation approach is further designed to better identify noisy interactions with the consideration of the above two types of interactions. Our framework BNCL offers a generic training paradigm that is applicable to different backbones. Extensive experiments on three shopping transaction datasets verify the effectiveness of our proposed method.

翻译：电子商务的发展见证了亚马逊、eBay和淘宝等平台的日益普及。这一趋势催生了一种独特的购物行为——"购物篮"，即用户同时购买的一组商品。作为学术界研究较少的交互模式，购物篮应如何补充个性化推荐系统的问题仍有待深入探索。以往研究侧重于联合建模用户购买行为与购物篮行为，但这两类元素在语义上的本质差异会导致直接融合时引入噪声。这种噪声会降低模型性能，而用户行为与购物篮行为中存在的显著噪声（例如，用户被误导点击某商品，或消费后认为其无趣）进一步加剧了负面影响。为应对上述挑战，我们提出了一种基于噪声容忍对比学习的购物篮推荐框架BNCL，旨在处理跨行为融合与行为内建模中的噪声问题。首先，我们将购物篮-商品交互表示为超图以建模复杂的购物篮行为，将同一购物篮中的所有商品视为单条超边。其次，设计跨行为对比学习抑制多种行为融合过程中的噪声。最后，为进一步削弱用户与购物篮交互中的行为内噪声，我们提出利用推荐系统对数据增强操作的不变性属性，通过行为内对比学习实现。同时，针对上述两类交互设计了一种新的一致性感知增强方法，以更准确地识别噪声交互。我们的BNCL框架提供了一种可适用于不同基础模型的通用训练范式。在三个购物交易数据集上的大量实验验证了所提方法的有效性。