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Abstract:
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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. |