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Estimating the impressionrank of web pages
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International World Wide Web Conference archive
Proceedings of the 18th international conference on World wide web table of contents
Madrid, Spain
SESSION: Data mining/session: graph algorithms table of contents
Pages 41-50  
Year of Publication: 2009
ISBN:978-1-60558-487-4
Authors
Ziv Bar-Yossef  Technion and Google Haifa Engineering Center, Haifa, Israel
Maxim Gurevich  Technion , Haifa, Israel
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

The ImpressionRank of a web page (or, more generally, of a web site) is the number of times users viewed the page while browsing search results. ImpressionRank captures the visibility of pages and sites in search engines and is thus an important measure, which is of interest to web site owners, competitors, market analysts, and end users.

All previous approaches to estimating the ImpressionRank of a page rely on privileged access to private data sources, like the search engine's query log. In this paper we present the first external algorithm for estimating the ImpressionRank of a web page. This algorithm relies on access to three public data sources: the search engine, the query suggestion service of the search engine, and the web. In addition, the algorithm is local and uses modest resources. It can therefore be used by almost any party to estimate the ImpressionRank of any page on any search engine.

En route to estimating the ImpressionRank of a page, our algorithm solves a novel variant of the keyword extraction problem: it finds the most popular search keywords that drive impressions of a page.

Empirical analysis of the algorithm on the Google and Yahoo! search engines indicates that it is accurate and provides interesting insights about sites and search queries.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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comScore. 61 billion searches conducted worldwide in August. www.comscore.com/press/release.asp?press=1802, 2008.
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J. Goodman and V. R. Carvalho. Implicit queries for email. In CEAS, July 2005.
 
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D. Kelleher and S. Luz. Automatic hypertext keyphrase detection. In 22nd IJCAI, 2005.
 
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P. D. Turney. Coherent keyphrase extraction via web mining. In 20th IJCAI, pages 434--439, 2003.
 
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M. B. Valentine. Google drives 70 percent of traffic to most web sites. http://searchengineoptimism.com/Google_refers_70_percent.html.
 
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Y. Xie and D. R. O'Hallaron. Locality in search engine queries and its implications for caching. In 21st INFOCOM, 2002.

Collaborative Colleagues:
Ziv Bar-Yossef: colleagues
Maxim Gurevich: colleagues