Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Label Smoothness Regularization 因为与传统的GNN不同，边的权重是可以学习的，而且在钱箱传播的过程中，还有w的参数需要学习。为了防止过拟合，而且由于边的权重的重要性，因此需要刚过的约束。作者在这里使用了Label Smoothness Regularization。具体的公示内容没有看懂。为了这个正则化和可学习的gnn网络，作者提出了一个独特的损失函数。 直观理
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相关文章
- 1. Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
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