research-article

Estimating the impressionrank of web pages

Authors:

Ziv Bar-Yossef,

Maxim GurevichAuthors Info & Claims

WWW '09: Proceedings of the 18th international conference on World wide web

Pages 41 - 50

https://doi.org/10.1145/1526709.1526716

Published: 20 April 2009 Publication History

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.

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Cited By

Khan OWasi SSiddiqui MKarim A(2022)Keyword Extraction for Medium-Sized Documents Using Corpus-Based Contextual Semantic SmoothingComplexity10.1155/2022/70157642022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7015764
Katzir LShikhelman CYogev E(2020)Interactive Proofs for Social GraphsAdvances in Cryptology – CRYPTO 202010.1007/978-3-030-56877-1_20(574-601)Online publication date: 17-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-56877-1_20
Katzir LHardiman S(2015)Estimating Clustering Coefficients and Size of Social Networks via Random WalkACM Transactions on the Web10.1145/27903049:4(1-20)Online publication date: 28-Sep-2015
https://dl.acm.org/doi/10.1145/2790304
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Index Terms

Estimating the impressionrank of web pages
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

WWW '09: Proceedings of the 18th international conference on World wide web

April 2009

1280 pages

ISBN:9781605584874

DOI:10.1145/1526709

General Chairs:
Juan Quemada
DIT-UPM
,
Gonzalo León
DIT-UPM
,
Program Chairs:
Yoelle Maarek
Google Inc., Israel
,
Wolfgang Nejdl
L3S and Hannover University

Copyright © 2009 IW3C2 org.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2009

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WWW '09

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WWW '09: The 18th International World Wide Web Conference

April 20 - 24, 2009

Madrid, Spain

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Khan OWasi SSiddiqui MKarim A(2022)Keyword Extraction for Medium-Sized Documents Using Corpus-Based Contextual Semantic SmoothingComplexity10.1155/2022/70157642022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7015764
Katzir LShikhelman CYogev E(2020)Interactive Proofs for Social GraphsAdvances in Cryptology – CRYPTO 202010.1007/978-3-030-56877-1_20(574-601)Online publication date: 17-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-56877-1_20
Katzir LHardiman S(2015)Estimating Clustering Coefficients and Size of Social Networks via Random WalkACM Transactions on the Web10.1145/27903049:4(1-20)Online publication date: 28-Sep-2015
https://dl.acm.org/doi/10.1145/2790304
Zhang MZhang NDas GHe QIyengar ANejdl WPei JRastogi R(2013)Mining a search engine's corpus without a query poolProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505748(29-38)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2505748
Hardiman SKatzir LSchwabe DAlmeida VGlaser HBaeza-Yates RMoon S(2013)Estimating clustering coefficients and size of social networks via random walkProceedings of the 22nd international conference on World Wide Web10.1145/2488388.2488436(539-550)Online publication date: 13-May-2013
https://dl.acm.org/doi/10.1145/2488388.2488436
Zhang MZhang NDas GCandan KChen YSnodgrass RGravano LFuxman A(2012)Aggregate suppression for enterprise search enginesProceedings of the 2012 ACM SIGMOD International Conference on Management of Data10.1145/2213836.2213890(469-480)Online publication date: 20-May-2012
https://dl.acm.org/doi/10.1145/2213836.2213890
Agosti MCrivellari FDi Nunzio G(2012)Web log analysisData Mining and Knowledge Discovery10.1007/s10618-011-0228-824:3(663-696)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1007/s10618-011-0228-8
Bar-Yossef ZGurevich M(2011)Efficient Search Engine MeasurementsACM Transactions on the Web10.1145/2019643.20196455:4(1-48)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1145/2019643.2019645
GM PLeela KParsana MGarg SKing INejdl WLi H(2011)Learning website hierarchies for keyword enrichment in contextual advertisingProceedings of the fourth ACM international conference on Web search and data mining10.1145/1935826.1935892(425-434)Online publication date: 9-Feb-2011
https://dl.acm.org/doi/10.1145/1935826.1935892
Agosti MCrivellari FDi Nunzio G(2011)Web log analysis: a review of a decade of studies about information acquisition, inspection and interpretation of user interactionData Mining and Knowledge Discovery10.1007/s10618-011-0236-8Online publication date: 6-Sep-2011
https://doi.org/10.1007/s10618-011-0236-8
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