A unified probabilistic framework for web page scoring systems

Abstract

The definition of efficient page ranking algorithms is becoming an important issue in the design of the query interface of Web search engines. Information flooding is a common experience especially when broad topic queries are issued. Queries containing only one or two keywords usually match a huge number of documents, while users can only afford to visit the first positions of the returned list, which do not necessarily refer to the most appropriate answers. Some successful approaches to page ranking in a hyperlinked environment, like the Web, are based on link analysis. We propose a general probabilistic framework for Web page scoring systems (WPSS), which incorporates and extends many of the relevant models proposed in the literature. In particular, we introduce scoring systems for both generic (horizontal) and focused (vertical) search engines. Whereas horizontal scoring algorithms are only based on the topology of the Web graph, vertical ranking also takes the page contents into account and are the base for focused and user adapted search interfaces. Experimental results are reported to show the properties of some of the proposed scoring systems with special emphasis on vertical search.