  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.
