N -Step PageRank for Web Search

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.