Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Graph Convolutional Neural Networks for Web-Scale Recommender Systems 摘要 作者們開發了一個數據高效的GCN算法PinSage，該算法聯合有效的random walk以及圖卷積來生成涵蓋圖結構和結點特徵的嵌入結點。相對於之前的GCN方法，作者提出了高效的random walk方法同時設計了一個新穎的訓練策略以提高模型的收斂性和魯
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相關文章
- 1. Knowledge Graph Convolutional Networks for Recommender Systems
- 2. 閱讀總結：《Graph Convolutional Neural Networks for Web-Scale Recommender Systems》
- 3. Graph Convolutional Neural Networks for Web-Scale Recommender Systems閱讀筆記
- 4. Side Information-Knowledge Graph Convolutional Networks for Recommender Systems閱讀筆記
- 5. 論文閱讀：Knowledge Graph Convolutional Networks for Recommender Systems
- 6. Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
- 7. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
- 8. Paper Notes: Graph Convolutional Neural Networks for Web-Scale Recommender Systemste
- 9. Graph Neural Networks for Social Recommendation
- 10. Adaptive Graph Convolutional Neural Networks