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.
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Zhang, L., Qin, T., Liu, TY., Bao, Y., Li, H. (2007). N-Step PageRank for Web Search. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_63
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DOI: https://doi.org/10.1007/978-3-540-71496-5_63
Publisher Name: Springer, Berlin, Heidelberg
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