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感谢关注!对您的问题,我提供两个理解角度:
- 我们的基本假设是“噪声占数据集的少数/绝对少数”。在RGCF中,中心节点聚合一阶邻居信息得到的结构特征对应了局部的”统计信息“,相对于单个节点的”个体信息“,”统计信息“面对数据中的少数噪声是更鲁棒的。因此,先用结构特征执行去噪,再进行正常的GNN传播会有不错的去噪效果。
- 去噪的过程本身也是学习的过程。在RGCF的初始状态(即基于无权交互图计算用户/商品表示),节点表示受到噪声的较大影响;但随着训练的进行,模型将逐渐自适应地降低噪声交互的权重并学得更好的表示。类似的“自己帮助自己”学习更好表征的**并不少见(如 self-attention mechanism)。
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