中心句: The features can thus be directly interpreted as users. These users will be referred to as representative users.
矩阵分解中的一个问题是隐因子无法解释，这篇文章避免了直接解释隐因子，巧妙的用用户来表示隐因子。
具体做法：假设用户的隐因子矩阵是一个转换矩阵，用one-hot向量分别标识用户和隐因子。转换矩阵的作用就是将用户向标识量转换成隐因子标识向量，完成用户和隐因子之间的对应关系。也就完成了用用户表达隐因子的作用。
有几个结论和几个值得探讨的问题：
1）文中这种具体操作中，会对转换矩阵做约束，如：让部分用户的隐因子向量变成单位向量。而文中实验表明，这种做法会一般会降低accuracy，但是降低的不多。
2）这种representative users是否可以迁移？
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| What about Interpreting Features in Matrix Factorization-based Recommender Systems as Users? | |||||||||||
| 文献作者 | Marharyta Aleksandrova | ||||||||||
| 文献发表年限 | 2014 | ||||||||||
| 文献关键字 | |||||||||||
| Recommender systems, matrix factorization, features interpretation. | |||||||||||
| 摘要描述 | |||||||||||
| Matrix factorization (MF) is a powerful approach used in recommender systems. One main drawback of MF is the difficulty to interpret the automatically formed features. Following the intuition that the relation between users and items can be expressed through a reduced set of users, referred to as representative users, we propose a simple modification of a traditional MF algorithm, that forms a set of features corresponding to these representative users. On one state of the art dataset, we show that the proposed representative users-based non-negative matrix factorization (RU-NMF) discovers interpretable features, while slightly (in some cases insignificantly) decreasing the accuracy. | |||||||||||
