Hyper-Class Representation of Data
This addresses data representation issues for recommendation systems, but appears incremental as it builds on existing divergence metrics and recommendation algorithms.
The authors tackled limitations in attribute-centered data processing by proposing a hyper-class representation for recommendation systems, showing that it provides useful reference information and makes recommendations much better than existing algorithms.
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.