Discovering the rating pattern of online reviewers through data coclustering

The reliability of online reviewers is a complex issue due to multiple dimensional and heterogeneous properties. In this paper, we propose a computational study of the rating pattern of reviewers. A reliability matrix is built between two data types which are the reviewer and the product. Two types of clusters can be extracted concurrently after a nonnegative tri-factorization of the reliability matrix. In our preliminary experiments, a rating consistency was used in formulation of the reliability matrix. The experimental results uncovered the rating patterns which reflects the product-dependent property of the reviewers. Our study demonstrated that the coclustering of reviewers and products can be a promising technique for analysis of online reviewers. The results from coclustering reviewers and products provides valuable knowledge for predicting the reliability of a reviewer to a product.