N-step PageRank for web search

  • Authors:

  • Li Zhang;Tao Qin;Tie-Yan Liu;Ying Bao;Hang Li

  • Affiliations:

  • Department of Mathematics, Beijing Jiaotong University, Beijing, P.R. China;MSPLAB, Dept. Electronic Engineering, Tsinghua University, Beijing, P.R. China;Microsoft Research Asia, Haidian District, Beijing, P.R. China;Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, P.R. China;Microsoft Research Asia, Haidian District, Beijing, P.R. China

  • Venue:
  • ECIR'07 Proceedings of the 29th European conference on IR research
  • Year:
  • 2007

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Abstract

PageRank has been widely used to measure the importance of web pages based on their interconnections in the web graph. Mathematically speaking, PageRank can be explained using a Markov random walk model, in which only the direct outlinks of a page contribute to its transition probability. In this paper, we propose improving the PageRank algorithm by looking N-step ahead when constructing the transition probability matrix. The motivation comes from the similar "looking N-step ahead" strategy that is successfully used in computer chess. Specifically, we assume that if the random surfer knows the N-step outlinks of each web page, he/she can make a better decision on choosing which page to navigate for the next time. It is clear that the classical PageRank algorithm is a special case of our proposed N-step PageRank method. Experimental results on the dataset of TREC Web track show that our proposed algorithm can boost the search accuracy of classical PageRank by more than 15% in terms of mean average precision.