Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. We point out that current sampling methods mainly focus on Point-wise or Line-wise sampling, lacking flexibility and leaving a significant portion of the hard sampling area un-explored. To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models. DINS comprises three modules: Hard Boundary Definition, Dimension Independent Mixup, and Multi-hop Pooling. Experiments with real-world datasets on both matrix factorization and graph-based models demonstrate that DINS outperforms other negative sampling methods, establishing its effectiveness and superiority. Our work contributes a new perspective, introduces Area-wise sampling, and presents DINS as a novel approach that achieves state-of-the-art performance for negative sampling. Our implementations are available in PyTorch.

翻译：协同过滤（CF）是一种基于历史交互预测用户偏好的广泛应用技术。负采样在基于隐式反馈训练CF模型过程中起着关键作用。本文提出一种基于采样区域的新视角，重新审视现有采样方法。我们指出现有采样方法主要聚焦于点状或线状采样，存在灵活性不足的缺陷，且大部分硬采样区域未被充分探索。为解决此局限性，我们提出面向硬负样本的维度无关混合方法（DINS），这是首个用于训练CF模型的区域状采样方法。DINS包含三个模块：硬边界定义、维度无关混合与多跳池化。在矩阵分解模型和图模型上基于真实数据集的实验表明，DINS优于其他负采样方法，验证了其有效性与优越性。我们的工作贡献了新视角，引入了区域状采样，并提出了实现负采样领域最先进性能的创新方法DINS。相关实现已基于PyTorch开源。