Using probabilistic latent semantic analysis for Web page grouping

Abstract

The locality of Web pages within a Web site is initially determined by the designer's expectation. Web usage mining can discover the patterns in the navigational behaviour of Web visitors, in turn, improve Web site functionality and service designing by considering users' actual opinion. Conventional Web page clustering technique is often utilized to reveal the functional similarity of Web pages. However, high-dimensional computation problem will be incurred due to taking user transaction as dimension. In this paper, we propose a new Web page grouping approach based on a probabilistic latent semantic analysis (PLSA) model. An iterative algorithm based on maximum likelihood principle is employed to overcome the aforementioned computational shortcoming. The Web pages are classified into various groups according to user access patterns. Meanwhile, the semantic latent factors or tasks are characterized by extracting the content of "dominant" pages related to the factors. We demonstrate the effectiveness of our approach by conducting experiments on real world data sets.