Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
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DOI：
10.1145/3583780.3615039
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Xinrui He;Tianxin Wei;Jingrui He
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文献类型：
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作者：
Xinrui He;Tianxin Wei;Jingrui He
随着电子商务的发展，亚马逊、eBay和淘宝等平台的人气激增。这就产生了一种独特的购物行为，包括篮子-一起购买的物品。购物篮作为社区中研究较少的交互模式，如何补充个性化推荐系统的问题仍然没有得到充分的探讨。虽然以前的尝试集中在联合建模用户购买和购物篮，这些元素的不同语义性质可以直接集成时引入噪音。这种噪声会对模型的性能产生负面影响，并因显著的噪声（例如，用户被误导点击项目或在消费项目之后将其识别为不感兴趣）。为了科普上述问题，本文提出了一种基于噪声容忍对比学习的Basket推荐框架（BNCL），用于处理跨行为集成和行为内建模中存在的噪声。首先，我们将篮子-物品交互表示为超图来模拟复杂的篮子行为，其中出现在同一篮子中的所有物品被视为单个超边。第二，设计了跨行为对比学习，以抑制不同行为融合过程中的噪声。接下来，为了进一步抑制用户和购物篮交互的行为内噪音，我们建议通过行为内对比学习来利用推荐者的不变属性进行增强。进一步设计了一种新的一致性增强方法，以更好地识别噪声交互与上述两种类型的相互作用的考虑。我们的框架BNCL提供了一个通用的训练范式，适用于不同的骨干。在三个购物交易数据集上的实验验证了该方法的有效性。
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 the 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.