论文笔记 《Neural Attentional Rating Regression with Review-level Explanations》
Intruduction 评分预测是推荐系统中常见的任务。大多数方法都是基于协同过滤的，例如PMF等，这类方法最大的问题在于无法对推荐提供可解释性。很多研究表明在推荐中提供可解释性，是非常有用的。缺乏可解释性的推荐不能真正让用户信服。在大多数电商网站中，允许用户评论产品并给予评分，用户评论信息中会通常包含关于产品有用的特征，例如quality, material, color 等。在本文中，评论的
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相关文章
- 1. WWW2018-Neural Attentional Rating Regression with Review-level Explanations
- 2. Neural Attentional Rating Regression with Review-level Explanations阅读笔记
- 3. 论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 4. 【论文笔记】Reasoning about Entailment with Neural Attention
- 5. 论文笔记：Exploring Neural Networks with Activation Atlases
- 6. 论文笔记《Neural Architecture Search With Reinforcement Learning》
- 7. 【论文笔记】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 8. 论文笔记：Neural Collaborative Filtering
- 9. 论文笔记之Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
- 10. 《User Modeling with Neural Network for Review Rating Prediction》评论打分预测