Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Graph Convolutional Neural Networks for Web-Scale Recommender Systems 摘要 作者们开发了一个数据高效的GCN算法PinSage，该算法联合有效的random walk以及图卷积来生成涵盖图结构和结点特征的嵌入结点。相对于之前的GCN方法，作者提出了高效的random walk方法同时设计了一个新颖的训练策略以提高模型的收敛性和鲁
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相关文章
- 1. Knowledge Graph Convolutional Networks for Recommender Systems
- 2. 阅读总结：《Graph Convolutional Neural Networks for Web-Scale Recommender Systems》
- 3. Graph Convolutional Neural Networks for Web-Scale Recommender Systems阅读笔记
- 4. Side Information-Knowledge Graph Convolutional Networks for Recommender Systems阅读笔记
- 5. 论文阅读：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