Applying probabilistic model for ranking Webs in multi-context

Abstract

The PageRank algorithm, used in the Google search engine, greatly improves the results of Web search by applying probabilistic model on the link structure of Webs to evaluate the “importance” of Webs. In PageRank probabilistic model, the links and webs are uniform, so the rank score of webs are quite independent from their content. In practice, the researchers often hope that the web results can be ranked by their proposed topics. Moreover, when computer’s techniques solve given problems ineffectively, it’s necessary to do better research in theoretical problems. From this judgement, in this paper, we introduce and describe the MPageRank based on a new probabilistic model supporting multi-context for ranking Webs. A Web now has different ranking scores, which depends on the given multi topics. The basic idea in establishing the new MPageRank model is that partition our Web graph into smaller-size sub Web graph. As a consequence of evaluation and rejection about pages influence weakly to other pages, the rank score of pages of the original Web graph can be approximated from the rank score of pages in the new partition Web graph. Similar to the PageRank, the multi ranking scores in the MPageRank are pre-computed and reflect the hyperlink of Web environment.