Hyper-Class Representation of Data
Abstract
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.
Cite
@article{arxiv.2201.13317,
title = {Hyper-Class Representation of Data},
author = {Shichao Zhang and Jiaye Li and Wenzhen Zhang and Yongsong Qin},
journal= {arXiv preprint arXiv:2201.13317},
year = {2022}
}