論文閱讀：Collaborative Knowledge Base Embedding for Recommender Systems
1. Motivation 稀疏性問題是推薦系統必須要面臨的一個挑戰，把side-information包含進基於MF的方法是一個很好的方式。但是物品的屬性是一種結構化的數據，用簡單的MLP並不能學習到它們之間的關聯，從而無法全面的刻畫一個物品。本文提出基於知識圖譜的embedding來刻畫物品的顯式信息，這種是從圖結構中學到的關聯，能夠比較充分地學習到物品的表達。本文關於物品的表達有三個部分，另
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相關文章
- 1. 論文閱讀：Knowledge Graph Convolutional Networks for Recommender Systems
- 2. 論文閱讀：《Wide & Deep Learning for Recommender Systems》
- 3. Collaborative Deep Learning for Recommender Systems
- 4. Knowledge Graph Convolutional Networks for Recommender Systems
- 5. Wide & Deep Learning for Recommender Systems 論文閱讀總結
- 6. Side Information-Knowledge Graph Convolutional Networks for Recommender Systems閱讀筆記
- 7. 論文閱讀：Collaborative Deep Embedding Via Dual Networks
- 8. 【論文導讀】MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS
- 9. 閱讀筆記--RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
- 10. Learning over Knowledge-Base Embeddings for Recommendation 論文