poster

SNDocRank: document ranking based on social networks

Authors:

Liang Gou,

Hung-Hsuan Chen,

Jung-Hyun Kim,

Xiaolong (Luke) Zhang,

C. Lee GilesAuthors Info & Claims

WWW '10: Proceedings of the 19th international conference on World wide web

Pages 1103 - 1104

https://doi.org/10.1145/1772690.1772825

Published: 26 April 2010 Publication History

Get Access

Abstract

To improve the search results for socially-connect users, we propose a ranking framework, Social Network Document Rank (SNDocRank). This framework considers both document contents and the similarity between a searcher and document owners in a social network and uses a Multi-level Actor Similarity (MAS) algorithm to efficiently calculate user similarity in a social network. Our experiment results based on YouTube data show that compared with the tf-idf algorithm, the SNDocRank method returns more relevant documents of interest. Our findings suggest that in this framework, a searcher can improve search by joining larger social networks, having more friends, and connecting larger local communities in a social network.

References

[1]

Clauset, A., M. E. J. Newman, & C. Moore. Finding community structure in very large networks. Phys. Rev. E, 70(6), 66111, 2004.

Crossref

Google Scholar

[2]

Gou, L., H. Chen, J. Kim, X. L. Zhang & C. L. Giles. SNDocRank: a Social Network-Based Video Search Ranking Framework. Proc. of ACM MIR'10.

Digital Library

Google Scholar

[3]

Gou, L., J. Kim, H. Chen, J. Collins, M. Goodman, X. L. Zhang & C. L. Giles. MobiSNA: a Mobile Video Social Network Application. Proc. of MobiDE'09: 53--56.

Digital Library

Google Scholar

[4]

Leicht, E. A., P. Holme & Newman, M. E. J. Vertex similarity in networks. APS, 73, 26120, 2006.

Google Scholar

[5]

Micarelli, A., F. Gasparetti, F. Sciarrone, & S. Gauch Personalized search on the World Wide Web. In Lecture Notes in Computer Science, 2007.

Google Scholar

[6]

Salton, G. & C. Buckley. Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513--523, 1988.

Digital Library

Google Scholar

[7]

Jarvelin, K. & J. Kekalainen. IR evaluation methods for retrieving highly relevant documents. In Proc. of SIGIR '00, pages 41--48, 2000.

Digital Library

Google Scholar

Cited By

View all

Fernandez-Luque LKarlsen RMelton G(2012)HealthTrust: A Social Network Approach for Retrieving Online Health VideosJournal of Medical Internet Research10.2196/jmir.198514:1(e22)Online publication date: 31-Jan-2012
https://doi.org/10.2196/jmir.1985
Lu HTing IWang S(2012)A Novel Search Engine Based on Social Relationships in Online Social Networking WebsiteProceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)10.1109/ASONAM.2012.191(1111-1116)Online publication date: 26-Aug-2012
https://dl.acm.org/doi/10.1109/ASONAM.2012.191
Karweg BHuetter CBöhm K(2011)Evolving social search based on bookmarks and status messages from social networksProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063839(1825-1834)Online publication date: 24-Oct-2011
https://dl.acm.org/doi/10.1145/2063576.2063839

Index Terms

SNDocRank: document ranking based on social networks
1. Information systems
  1. Information retrieval

Recommendations

Social network document ranking
JCDL '10: Proceedings of the 10th annual joint conference on Digital libraries

In search engines, ranking algorithms measure the importance and relevance of documents mainly based on the contents and relationships between documents. User attributes are usually not considered in ranking. This user-neutral approach, however, may not ...
SNDocRank: a social network-based video search ranking framework
MIR '10: Proceedings of the international conference on Multimedia information retrieval

Multimedia ranking algorithms are usually user-neutral and measure the importance and relevance of documents by only using the visual contents and meta-data. However, users' interests and preferences are often diverse, and may demand different results ...
Effective rank aggregation for metasearching

Nowadays, mashup services and especially metasearch engines play an increasingly important role on the Web. Most of users use them directly or indirectly to access and aggregate information from more than one data sources. Similarly to the rest of the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

WWW '10: Proceedings of the 19th international conference on World wide web

April 2010

1407 pages

ISBN:9781605587998

DOI:10.1145/1772690

General Chairs:
Michael Rappa
North Carolina State University, USA
,
Paul Jones
University of North Carolina at Chapel Hill, USA
,
Program Chairs:
Juliana Freire
University of Utah, USA
,
Soumen Chakrabarti
Indian Institute of Technology, India

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

WWW '10

WWW '10: The 19th International World Wide Web Conference

April 26 - 30, 2010

North Carolina, Raleigh, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
403
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Fernandez-Luque LKarlsen RMelton G(2012)HealthTrust: A Social Network Approach for Retrieving Online Health VideosJournal of Medical Internet Research10.2196/jmir.198514:1(e22)Online publication date: 31-Jan-2012
https://doi.org/10.2196/jmir.1985
Lu HTing IWang S(2012)A Novel Search Engine Based on Social Relationships in Online Social Networking WebsiteProceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)10.1109/ASONAM.2012.191(1111-1116)Online publication date: 26-Aug-2012
https://dl.acm.org/doi/10.1109/ASONAM.2012.191
Karweg BHuetter CBöhm K(2011)Evolving social search based on bookmarks and status messages from social networksProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063839(1825-1834)Online publication date: 24-Oct-2011
https://dl.acm.org/doi/10.1145/2063576.2063839

View Options

Get Access

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.

PDF

eReader

View online with eReader.

eReader

ePub

View this article in ePub.

ePub

Abstract

References

Cited By

Index Terms

Recommendations

Social network document ranking

SNDocRank: a social network-based video search ranking framework

Effective rank aggregation for metasearching

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

ePub

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations