Negative sampling plays a crucial role in training successful sequential recommendation models. Instead of merely employing random negative sample selection, numerous strategies have been proposed to mine informative negative samples to enhance training and performance. However, few of these approaches utilize structural information. In this work, we observe that as training progresses, the distributions of node-pair similarities in different groups with varying degrees of neighborhood overlap change significantly, suggesting that item pairs in distinct groups may possess different negative relationships. Motivated by this observation, we propose a Graph-based Negative sampling approach based on Neighborhood Overlap (GNNO) to exploit structural information hidden in user behaviors for negative mining. GNNO first constructs a global weighted item transition graph using training sequences. Subsequently, it mines hard negative samples based on the degree of overlap with the target item on the graph. Furthermore, GNNO employs curriculum learning to control the hardness of negative samples, progressing from easy to difficult. Extensive experiments on three Amazon benchmarks demonstrate GNNO's effectiveness in consistently enhancing the performance of various state-of-the-art models and surpassing existing negative sampling strategies. The code will be released at \url{https://github.com/floatSDSDS/GNNO}.

翻译：负采样在训练成功的序列推荐模型中起着关键作用。除了简单地采用随机负样本选择外，许多策略已被提出以挖掘信息丰富的负样本来提升训练和性能。然而，这些方法中很少利用结构信息。在这项工作中，我们观察到随着训练的进行，不同邻域重叠程度的组中节点对相似度的分布发生显著变化，这表明不同组中的项目对可能具有不同的负关系。受此观察启发，我们提出了一种基于邻域重叠的图负采样方法（GNNO），以利用用户行为中隐藏的结构信息进行负样本挖掘。GNNO首先使用训练序列构建全局加权项目转移图，然后根据与目标项目在图上的重叠程度挖掘硬负样本。此外，GNNO采用课程学习来控制负样本的难度，从简单到渐进。在三个亚马逊基准数据集上的大量实验表明，GNNO在持续增强各种最先进模型的性能并超越现有负采样策略方面具有有效性。代码将在\url{https://github.com/floatSDSDS/GNNO}发布。