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.

本文提出了一种基于采样区域的负采样方法：DINS，与现有的采样方法有所不同，DINS 的三个模块分别是难负样本界定，维度无关混合和多跳池化，实验表明 DINS 优于其他采样方法，成为一种实现最佳性能的负采样方法。