论文阅读：Knowledge Graph Convolutional Networks for Recommender Systems
1. Motivation 融合吸收side-information是缓解CF冷启动问题的一种解决方案，但是真实场景中的物品属性并不是isolated，它们之间彼此相连。简单的拼接或者非线性交互并不能很好的提取到这些属性之间的关联。为此，我们可以将物品和描述物品的属性放在一张知识图谱里面考虑，并且用GCN的方式吸收邻居的表达，并且随着迭代次数的增加，可以提取到用户“长距离”的兴趣，这在普通MLP中
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相关文章
- 1. Knowledge Graph Convolutional Networks for Recommender Systems
- 2. Side Information-Knowledge Graph Convolutional Networks for Recommender Systems阅读笔记
- 3. Graph Convolutional Neural Networks for Web-Scale Recommender Systems
- 4. 阅读总结：《Graph Convolutional Neural Networks for Web-Scale Recommender Systems》
- 5. Graph Convolutional Neural Networks for Web-Scale Recommender Systems阅读笔记
- 6. Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
- 7. 论文阅读：《Wide & Deep Learning for Recommender Systems》
- 8. 阅读笔记--RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
- 9. 论文阅读：Collaborative Knowledge Base Embedding for Recommender Systems
- 10. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems