Minghai Liu, Rui Cai, Ming Zhang, and Lei Zhang
24 October 2011
To optimize the performance of web crawlers, various measures of page importance have been studied to select and order URLs in crawling. Most sophisticated measures (e.g. breadth-ﬁrst and PageRank) are based on link structure. In this paper, we treat the problem from another perspective and propose to directly measure page importance through mining user interest and behaviors from web browse logs. Unlike most existing approaches which work on single URL, in this paper, both the log mining and the crawl ordering are performed at the granularity of URL pattern. The proposed URL pattern-based crawl orderings are capable to properly predict the importance of newly created (unseen) URLs. Promising experimental results proved the feasibility of our approach.
In in Proc. of the 20th ACM Conference on Information and Knowledge Management (CIKM 2011)
Publisher Association for Computing Machinery, Inc.
Copyright © 2011 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or firstname.lastname@example.org. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.