NewPR-Combining TFIDF with pagerank

Authors:
Hao-ming Wang;Martin Rajman;Ye Guo;Bo-qin Feng
Affiliations:
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China;School of I & C, Swiss Federal Institute of Technology(EPFL), Lausanne, Switzerland;School of Information, Xi'an University of Finance & Economics, Xi'an, Shaanxi, P.R. China;School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

TFIDF was widely used in IR system based on the vector space model (VSM). Pagerank was used in systems based on hyperlink structure such as Google. It was necessary to develop a technique combining the advantages of two systems. In this paper, we drew up a framework by using the content of web pages and the out-link information synchronously. We set up a matrix M, which composed of out-link information and the relevant value of web pages with the given query. The relevant value was denoted by TFIDF. We got the NewPR (New Pagerank) by solving the equation with the coefficient M. Experimental results showed that more pages, which were more important both in content and hyper-link sides, were selected.