論文筆記 《Neural Attentional Rating Regression with Review-level Explanations》
Intruduction 評分預測是推薦系統中常見的任務。大多數方法都是基於協同過濾的，例如PMF等，這類方法最大的問題在於無法對推薦提供可解釋性。很多研究表明在推薦中提供可解釋性，是非常有用的。缺乏可解釋性的推薦不能真正讓用戶信服。在大多數電商網站中，允許用戶評論產品並給予評分，用戶評論信息中會通常包含關於產品有用的特徵，例如quality, material, color 等。在本文中，評論的
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相關文章
- 1. WWW2018-Neural Attentional Rating Regression with Review-level Explanations
- 2. Neural Attentional Rating Regression with Review-level Explanations閱讀筆記
- 3. 論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 4. 【論文筆記】Reasoning about Entailment with Neural Attention
- 5. 論文筆記：Exploring Neural Networks with Activation Atlases
- 6. 論文筆記《Neural Architecture Search With Reinforcement Learning》
- 7. 【論文筆記】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 8. 論文筆記：Neural Collaborative Filtering
- 9. 論文筆記之Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
- 10. 《User Modeling with Neural Network for Review Rating Prediction》評論打分預測