TAILIEUCHUNG - Báo cáo "Applying probabilistic model for ranking Webs in multi-context "

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. . | VNU Journal of Science Mathematics - Physics 23 2007 35-46 Applying probabilistic model for ranking Webs in multi-context Le Trung Kien1 Tran Loc Hung1 Le Anh Vu2 1 Department of Mathematics Hue University of Sciences Vietnam 77 Nguyen Hue Hue city 2Department of Computer Science ELTE University Hungary Received 15 May 2007 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. 1. Introduction Nowadays the World Wide Web has became very large and heterogeneous with an extraordinary grow rate. It creates many new challenges for information retrieval. One of the interesting problems is that evaluating the importance of a Web. The search engines have to choose from a huge number of the Web pages which contain the information specified by the user the most important ones and

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