More Web Proxy on the site http://driver.im/

research-article

Wisdom of the ages: toward delivering the children's web with the link-based agerank algorithm

Authors:

Karl Gyllstrom,

Marie-Francine MoensAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 159 - 168

https://doi.org/10.1145/1871437.1871462

Published: 26 October 2010 Publication History

Abstract

Though children frequently use web search engines to learn, interact, and be entertained, modern web search engines are poorly suited to children's needs, requiring relatively complex querying and filtering of results in order to find pages oriented to young audiences. To address this limitation, we designed AgeRank, a link-based algorithm that ranks web pages according their appropriateness for young audiences. We show its effectiveness through a multipart evaluation that demonstrates AgeRank to be accurate in page-labeling, widely-spanning in page coverage, and with high potential to improve children's search. As a fast, scalable, and effective algorithm, AgeRank can be adopted by search engines seeking to more effectively address the needs of young users, or easily fitted to complementary machine-learning based classification approaches.

References

[1]

Amazon Mechanical Turk. http://www.mturk.com/.

[2]

The Clueweb09 dataset. http://boston.lti.cs.cmu.edu/Data/clueweb09/.

[3]

KidsClick!: Project background. http://www.kidsclick.org/clickback.html.

[4]

ODP - Open Directory Project. http://www.dmoz.org/.

[5]

Query Suggest FAQ. http://labs.google.com/intl/en/suggestfaq.html.

[6]

UK children's media literacy. http://www.ofcom.org.uk/advice/media_literacy/medlitpub/medlitpubrss/ukchildrensml/ukchildrensml1.pdf.

[7]

What is Yahoo! Kids? http://sp.askkids.com/docs/askkids/index.shtml.

[8]

D. Bilal. Children's use of the Yahooligans! web search engine (III). cognitive and physical behaviors on fully self-generated search tasks. J. Am. Soc. Inf. Sci. Technol., 53(13):1170--1183, 2002.

Digital Library

[9]

D. Bilal and J. Kirby. Differences and similarities in information seeking: children and adults as Web users. Information Processing & Management, 38(5):649--670, 2002.

Digital Library

[10]

S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst., 30(1-7):107--117, 1998.

Digital Library

[11]

J. Dean and S. Ghemawat. MapReduce: simplified data processing on large clusters. Commun. ACM, 51(1):107--113, 2008.

Digital Library

[12]

A. Druin, E. Foss, L. Hatley, E. Golub, M. L. Guha, J. Fails, and H. Hutchinson. How children search the internet with keyword interfaces. In IDC '09, pages 89--96, New York, NY, USA, 2009. ACM.

Digital Library

[13]

G. W. Flake, S. Lawrence, and C. L. Giles. Efficient identification of Web communities. In KDD '00, pages 150--160, New York, NY, USA, 2000. ACM.

Digital Library

[14]

Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen. Combating Web spam with Trustrank. In VLDB '04, pages 576--587. VLDB Endowment, 2004.

Digital Library

[15]

T. H. Haveliwala. Topic-sensitive PageRank. In WWW '02, pages 517--526, New York, NY, USA, 2002. ACM.

Digital Library

[16]

K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst., 20(4):422--446, 2002.

Digital Library

[17]

T. Sakai. Alternatives to Bpref. In SIGIR '07, pages 71--78, New York, NY, USA, 2007. ACM.

Digital Library

[18]

X. Zhu and Z. Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002.

Cited By

Allen GWright KFails JKennington CPera M(2023)Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00050(311-317)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00050
R. RTiwari HPatel JR. RR. K(2020)Bidirectional GRU-Based Attention Model for Kid-Specific URL ClassificationDeep Learning Techniques and Optimization Strategies in Big Data Analytics10.4018/978-1-7998-1192-3.ch005(78-90)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-1192-3.ch005
Rajalakshmi RTiwari HPatel JKumar AKarthik. R(2020)Design of Kids-specific URL Classifier using Recurrent Convolutional Neural NetworkProcedia Computer Science10.1016/j.procs.2020.03.260167(2124-2131)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.03.260
Show More Cited By

Index Terms

Wisdom of the ages: toward delivering the children's web with the link-based agerank algorithm
1. Information systems
  1. Information retrieval

Recommendations

Evaluating leading web search engines on children's queries
HCII'11: Proceedings of the 14th international conference on Human-computer interaction: users and applications - Volume Part IV

This study compared retrieved results, relevance ranking, and overlap across Google, Yahoo!, Bing, Yahoo Kids!, and Ask Kids on 15 queries constructed by middle school children. Queries included one word, two words, and multiple words/phrases/natural ...
PageRank revisited

PageRank, one part of the search engine Google, is one of the most prominent link-based rankings of documents in the World Wide Web. Usually it is described as a Markov chain modeling a specific random surfer. In this article, an alternative ...
Usability and perception of young users and adults on targeted web search engines
IIiX '14: Proceedings of the 5th Information Interaction in Context Symposium

The usability of web search engines is an important factor that influences user experience and correlates with users' success in finding the relevant information. Currently, there are different search engines online available whose main target audience ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part 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, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM '10

Sponsor:

CIKM '10: International Conference on Information and Knowledge Management

October 26 - 30, 2010

ON, Toronto, Canada

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
265
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)1

Reflects downloads up to 17 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Allen GWright KFails JKennington CPera M(2023)Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00050(311-317)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00050
R. RTiwari HPatel JR. RR. K(2020)Bidirectional GRU-Based Attention Model for Kid-Specific URL ClassificationDeep Learning Techniques and Optimization Strategies in Big Data Analytics10.4018/978-1-7998-1192-3.ch005(78-90)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-1192-3.ch005
Rajalakshmi RTiwari HPatel JKumar AKarthik. R(2020)Design of Kids-specific URL Classifier using Recurrent Convolutional Neural NetworkProcedia Computer Science10.1016/j.procs.2020.03.260167(2124-2131)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.03.260
Tseng HChen HChang KSung YChen B(2019)An Innovative BERT-Based Readability ModelInnovative Technologies and Learning10.1007/978-3-030-35343-8_32(301-308)Online publication date: 18-Nov-2019
https://doi.org/10.1007/978-3-030-35343-8_32
Ojha PIsmail AKuppusamy K(2018)Perusal of readability with focus on web content understandabilityJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2018.03.007Online publication date: Mar-2018
https://doi.org/10.1016/j.jksuci.2018.03.007
Pera MNg Y(2017)Using online data sources to make query suggestions for childrenWeb Intelligence10.3233/WEB-17036715:4(303-323)Online publication date: 20-Nov-2017
https://doi.org/10.3233/WEB-170367
Sasaki YKomatsuda TKeyaki AMiyazaki JAnderst-Kotsis G(2016)A new readability measure for web documents and its evaluation on an effective web search engineProceedings of the 18th International Conference on Information Integration and Web-based Applications and Services10.1145/3011141.3011172(355-362)Online publication date: 28-Nov-2016
https://dl.acm.org/doi/10.1145/3011141.3011172
Foss EDruin A(2014)Children’s Internet Search: Using Roles to Understand Children’s Search BehaviorSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00591ED1V01Y201408ICR0346:2(1-106)Online publication date: 22-Sep-2014
https://doi.org/10.2200/S00591ED1V01Y201408ICR034
Gossen TMüller RStober SNürnberger AThomsen BElbæk LIversen OMarkopoulos PGarzotto FDindler C(2014)Search result visualization with characters for childrenProceedings of the 2014 conference on Interaction design and children10.1145/2593968.2593983(125-134)Online publication date: 17-Jun-2014
https://dl.acm.org/doi/10.1145/2593968.2593983
Duarte Torres SWeber IHiemstra D(2014)Analysis of Search and Browsing Behavior of Young Users on the WebACM Transactions on the Web10.1145/25555958:2(1-54)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1145/2555595
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents