Journal article
Topical web crawlers: Evaluating adaptive algorithms
ACM transactions on Internet technology, Vol.4(4), pp.378-419
11/2004
DOI: 10.1145/1031114.1031117
Abstract
Topical crawlers are increasingly seen as a way to address the scalability limitations of universal search engines, by distributing the crawling process across users, queries, or even client computers. The context available to such crawlers can guide the navigation of links with the goal of efficiently locating highly relevant target pages. We developed a framework to fairly evaluate topical crawling algorithms under a number of performance metrics. Such a framework is employed here to evaluate different algorithms that have proven highly competitive among those proposed in the literature and in our own previous research. In particular we focus on the tradeoff between exploration and exploitation of the cues available to a crawler, and on adaptive crawlers that use machine learning techniques to guide their search. We find that the best performance is achieved by a novel combination of explorative and exploitative bias, and introduce an evolutionary crawler that surpasses the performance of the best nonadaptive crawler after sufficiently long crawls. We also analyze the computational complexity of the various crawlers and discuss how performance and complexity scale with available resources. Evolutionary crawlers achieve high efficiency and scalability by distributing the work across concurrent agents, resulting in the best performance/cost ratio.
Details
- Title: Subtitle
- Topical web crawlers: Evaluating adaptive algorithms
- Creators
- Filippo Menczer - Indiana University, Bloomington, INGautam Pant - University of Utah, Salt Lake City, UTPadmini Srinivasan - University of Iowa, Iowa City, IA
- Resource Type
- Journal article
- Publication Details
- ACM transactions on Internet technology, Vol.4(4), pp.378-419
- DOI
- 10.1145/1031114.1031117
- ISSN
- 1533-5399
- eISSN
- 1557-6051
- Language
- English
- Date published
- 11/2004
- Academic Unit
- Business Analytics; Nursing; Computer Science
- Record Identifier
- 9984003015502771
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