論文閱讀：Knowledge Graph Convolutional Networks for Recommender Systems
1. Motivation 融合吸收side-information是緩解CF冷啓動問題的一種解決方案，但是真實場景中的物品屬性並不是isolated，它們之間彼此相連。簡單的拼接或者非線性交互並不能很好的提取到這些屬性之間的關聯。爲此，我們可以將物品和描述物品的屬性放在一張知識圖譜裏面考慮，並且用GCN的方式吸收鄰居的表達，並且隨着迭代次數的增加，可以提取到用戶「長距離」的興趣，這在普通MLP中
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- 1. Knowledge Graph Convolutional Networks for Recommender Systems
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- 3. Graph Convolutional Neural Networks for Web-Scale Recommender Systems
- 4. 閱讀總結：《Graph Convolutional Neural Networks for Web-Scale Recommender Systems》
- 5. Graph Convolutional Neural Networks for Web-Scale Recommender Systems閱讀筆記
- 6. Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
- 7. 論文閱讀：《Wide & Deep Learning for Recommender Systems》
- 8. 閱讀筆記--RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
- 9. 論文閱讀：Collaborative Knowledge Base Embedding for Recommender Systems
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相關文章
- 1. Knowledge Graph Convolutional Networks for Recommender Systems
- 2. Side Information-Knowledge Graph Convolutional Networks for Recommender Systems閱讀筆記
- 3. Graph Convolutional Neural Networks for Web-Scale Recommender Systems
- 4. 閱讀總結：《Graph Convolutional Neural Networks for Web-Scale Recommender Systems》
- 5. Graph Convolutional Neural Networks for Web-Scale Recommender Systems閱讀筆記
- 6. Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
- 7. 論文閱讀：《Wide & Deep Learning for Recommender Systems》
- 8. 閱讀筆記--RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
- 9. 論文閱讀：Collaborative Knowledge Base Embedding for Recommender Systems
- 10. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems