Data representation is usually a natural form with their attribute values. On this basis, data processing is an attribute-centered calculation. However, there are three limitations in the attribute-centered calculation, saying, inflexible calculation, preference computation, and unsatisfactory output. To attempt the issues, a new data representation, named as hyper-classes representation, is proposed for improving recommendation. First, the cross entropy, KL divergence and JS divergence of features in data are defined. And then, the hyper-classes in data can be discovered with these three parameters. Finally, a kind of recommendation algorithm is used to evaluate the proposed hyper-class representation of data, and shows that the hyper-class representation is able to provide truly useful reference information for recommendation systems and makes recommendations much better than existing algorithms, i.e., this approach is efficient and promising.

翻译：数据表述通常是一种自然形式,带有属性值。 在此基础上, 数据处理是一种以属性为中心的计算方法。 但是, 以属性为中心的计算有三个局限性, 即不灵活的计算、 偏重计算和不令人满意的输出。 为了尝试这些问题, 提议了一个新的数据表述方法, 称为超等表示法, 来改进建议。 首先, 定义了数据特征的跨星云、 KL 差异和 JS 差异。 然后, 数据中的超级分类可以与这三个参数一起发现。 最后, 使用一种建议算法来评价拟议的超级数据表述法, 并表明超级代表法能够为建议系统提供真正有用的参考信息, 并比现有的算法更好提出建议, 也就是说, 这种方法既有效又有希望。