論文閱讀 KGAT: Knowledge Graph Attention Network for Recommendation
1. Motivation 如何將side information和用戶-物品二部圖考慮在一起給用戶提供準確、多樣和可解釋的推薦是非常有必要的。目前基於CF的方法並不能很好的對用戶和物品的side information進行建模，這樣就不可避免的面臨數據稀疏性的問題。工業界的解決辦法是使用監督學習模型，比如FM，NFM，wide&deep等方法去將用戶物品的所有side information看成
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相關文章
- 1. 20200427 Knowledge Graph Attention Network for Recommendation
- 2. Side Information-KGAT: Knowledge Graph Attention Network for Recommendation閱讀筆記
- 3. 【論文翻譯】KGAT: Knowledge Graph Attention Network for Recommendation
- 4. Knowledge Graph Attention Network for Recommendation
- 5. Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
- 6. 論文閱讀《Graph Contextualized Self-Attention Network for Session-based Recommendation》
- 7. RKGE Recurrent Knowledge Graph Embedding for Effective Recommendation 論文
- 8. DKN: Deep Knowledge-Aware Network for News Recommendation閱讀筆記
- 9. Residual Attention Network for Image Classification 論文閱讀
- 10. 《Graph Attention Network》閱讀筆記