DistanceRank: An intelligent ranking algorithm for web pages

by: Zareh, Nasser Yazdani

Information Processing & Management, Vol. 44, No. 2. (March 2008), pp. 877-892.

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Abstract

A fast and efficient page ranking mechanism for web crawling and retrieval remains as a challenging issue. Recently, several link based ranking algorithms like PageRank, HITS and OPIC have been proposed. In this paper, we propose a novel recursive method based on reinforcement learning which considers distance between pages as punishment, called "DistanceRank" to compute ranks of web pages. The distance is defined as the number of "average clicks" between two pages. The objective is to minimize punishment or distance so that a page with less distance to have a higher rank. Experimental results indicate that DistanceRank outperforms other ranking algorithms in page ranking and crawling scheduling. Furthermore, the complexity of DistanceRank is low. We have used University of California at Berkeley's web for our experiments.

@article{citeulike:2879546, abstract = {A fast and efficient page ranking mechanism for web crawling and retrieval remains as a challenging issue. Recently, several link based ranking algorithms like PageRank, HITS and OPIC have been proposed. In this paper, we propose a novel recursive method based on reinforcement learning which considers distance between pages as punishment, called "DistanceRank" to compute ranks of web pages. The distance is defined as the number of "average clicks" between two pages. The objective is to minimize punishment or distance so that a page with less distance to have a higher rank. Experimental results indicate that DistanceRank outperforms other ranking algorithms in page ranking and crawling scheduling. Furthermore, the complexity of DistanceRank is low. We have used University of California at Berkeley's web for our experiments.}, author = {Zareh and Yazdani, Nasser }, booktitle = {Evaluating Exploratory Search Systems; Digital Libraries in the Context of Users' Broader Activities}, citeulike-article-id = {2879546}, doi = {10.1016/j.ipm.2007.06.004}, journal = {Information Processing \& Management}, keywords = {link-analysis, ranking}, month = {March}, number = {2}, pages = {877--892}, posted-at = {2008-06-10 14:28:20}, priority = {2}, title = {DistanceRank: An intelligent ranking algorithm for web pages}, url = {http://dx.doi.org/10.1016/j.ipm.2007.06.004}, volume = {44}, year = {2008} }

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TY - JOUR ID - citeulike:2879546 L3 - citeulike-article-id:2879546 TI - DistanceRank: An intelligent ranking algorithm for web pages T2 - Evaluating Exploratory Search Systems; Digital Libraries in the Context of Users' Broader Activities JF - Information Processing & Management VL - 44 IS - 2 SP - 877 EP - 892 N2 - A fast and efficient page ranking mechanism for web crawling and retrieval remains as a challenging issue. Recently, several link based ranking algorithms like PageRank, HITS and OPIC have been proposed. In this paper, we propose a novel recursive method based on reinforcement learning which considers distance between pages as punishment, called "DistanceRank" to compute ranks of web pages. The distance is defined as the number of "average clicks" between two pages. The objective is to minimize punishment or distance so that a page with less distance to have a higher rank. Experimental results indicate that DistanceRank outperforms other ranking algorithms in page ranking and crawling scheduling. Furthermore, the complexity of DistanceRank is low. We have used University of California at Berkeley's web for our experiments. KW - link-analysis KW - ranking AU - Zareh AU - Yazdani, Nasser PY - 2008/03// UR - http://dx.doi.org/10.1016/j.ipm.2007.06.004 ER -

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