所谓的 The Aspect-Item Recommender System，
其实就是把item的属性（Aspect，如哪个导演，哪个演员，哪个类型）考虑进来。
文中通过给定义几个评分计算方法，如用户对某个item的CF评分，用户对某个导演的评分。构建一个图。
最后的推荐是基于图中已经计算出的评分给出的（即，这个是一个基于内存的model，不是学习模型）。
基于以上构造的graph，进一步可以推到，哪个aspect对最终的推荐起到了关键促进作用。即作为推荐的解释。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them | |||||||||||
| 文献作者 | Antonio Rago, Oana Cocarascu, Francesca Toni | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| interpretation; A-I model; aspect-item model；可解释性；memory based; memory-based；公式很漂亮 | |||||||||||
| 摘要描述 | |||||||||||
| A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations. | |||||||||||
