Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Label Smoothness Regularization 因爲與傳統的GNN不同，邊的權重是可以學習的，而且在錢箱傳播的過程中，還有w的參數需要學習。爲了防止過擬合，而且由於邊的權重的重要性，因此需要剛過的約束。作者在這裏使用了Label Smoothness Regularization。具體的公示內容沒有看懂。爲了這個正則化和可學習的gnn網絡，作者提出了一個獨特的損失函數。 直觀理
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相關文章
- 1. Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
- 2. Graph Convolutional Neural Networks for Web-Scale Recommender Systems
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