| N-Step PageRank for Web Search (2008) | |||||||||||||||
Abstract | |||||||||||||||
| 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. 1 | |||||||||||||||
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