Learning probabilistic models of the Web (poster session)
In the World Wide Web, myriads of hyperlinks connect documents and pages to create an unprecedented, highly complex graph structure - the Web graph. This paper presents a novel approach to learning probabilistic models of the Web, which can be used to make reliable predictions about connectivity and information content of Web documents. The proposed method is a probabilistic dimension reduction technique which recasts and unites Latent Semantic Analysis and Kleinberg's Hubs-and-Authorities algorithm in a statistical setting.
This meant to be a first step towards the development of a statistical foundation for Web—related information technologies. Although this paper does not focus on a particular application, a variety of algorithms operating in the Web/Internet environment can take advantage of the presented techniques, including search engines, Web crawlers, and information agent systems.