所谓的 Multi-View其实对应的是语义特征的层次结构，例如虾是海鲜的子类（父类和子类关系，这点跟我们AE-MemNN算法很像）。
利用用户对物品的评价文本，可以为用户/item提取能够表达语义的关键词作为user/item的显性特征，这些所有特征就可以构造一个层次结构（Microsoft Concept Graph），我们为每一个显性特征，也就是图中的每一个顶点学习出一个vector representation，再利用子节点的表达，逐渐构造上层的表达（attention model），然后每一层都有一个表达，即multi-view。其实每一层都可以单独拿出来作为EFM（显性特征拼接隐式特征）的component，但本文有多层，每一层都可以用EFM，最后再利用attention model组合他们最后的预测评分。
在可解释性方面，只要学出哪些显性特征在最后的推荐中起到很大作用，如权重很大，就可认为是推荐此item的语义解释。（在如何选择有作用的特征上，本文提出了动态规划求解方法）
在验证可解释性性能的时候，本文是利用志愿者的参与进行评分的。其中为了验证算法的鲁棒性，本文还对志愿者进行了分组，发现不同的组表现都一样好。说明算法鲁棒性很好。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Explainable Recommendation Through Attentive Multi-View Learning | |||||||||||
| 文献作者 | Jingyue Gao; Xing Xie | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| attention/attentive model； review information; explicit feature hierarchy (hierarchical); Microsoft Concept Graph; 雇人验证算法解释性好坏；算法鲁棒性robust验证（对用户分组验证结果不变） | |||||||||||
| 摘要描述 | |||||||||||
| Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods. The basic idea is to build an initial network based on an explainable deep hierarchy (e.g., Microsoft Concept Graph) and improve the model accuracy by optimizing key variables in the hierarchy (e.g., node importance and relevance). To ensure accurate rating prediction, we propose an attentive multi-view learning framework. The framework enables us to handle sparse and noisy data by co-regularizing among different feature levels and combining predictions attentively. To mine readable explanations from the hierarchy, we formulate personalized explanation generation as a constrained tree node selection problem and propose a dynamic programming algorithm to solve it. Experimental results show that our model outperforms state-of-the-art methods in terms of both accuracy and explainability. | |||||||||||
