Article

Discovering and Modelling Multiple Interests of Users in Collaborative Tagging Systems

Authors:

WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03

Pages 115 - 118

https://doi.org/10.1109/WIIAT.2008.267

Published: 09 December 2008 Publication History

Publisher Site Get Access

Abstract

We analyse data obtained from several collaborative tagging systems and discover that user interests can be very diverse. Traditional methods for representing interests of users are usually not able to reflect such diversity. We propose a method to construct user profiles of multiple interests using data in a collaborative tagging system. Our evaluation suggests that the proposed method is able to generate user profiles which reflect the diversity of user interests and can be used to help provide more focused recommendation.

References

[1]

Marko Balabanovic and Yoav Shoham. Learning information retrieval agents: Experiments with automated web browsing. In Proceedings of the AAAI Spring Symposium on Information Gathering from Heterogenous, Distributed Resources, March, 1995, Stanford, CA, USA, pages 13-18, 1995.

Google Scholar

[2]

Jörg Diederich and Tereza Iofciu. Finding communities of practice from user profiles based on folksonomies. In Proceedings of the 1st International Workshop on Building Technology Enhanced Learning solutions for Communities of Practice, 2006.

Google Scholar

[3]

Daniela Godoy and Analia Amandi. User profiling in personal information agents: a survey. Knowl. Eng. Rev., 20(4):329-361, 2005.

Digital Library

Google Scholar

[4]

Andreas Hotho, Robert Jäschke, Christoph Schmitz, and Gerd Stumme. Information retrieval in folksonomies: Search and ranking. In York Sure and John Domingue, editors, The Semantic Web: Research and Applications, volume 4011 of LNCS, pages 411-426. Springer, June 2006.

Digital Library

Google Scholar

[5]

Hyung Joon Kook. Profiling multiple domains of user interests and using them for personalized web support. In De-Shuang Huang, Xiao-Ping Zhang, and Guang-Bin Huang, editors, Proceedings of International Conference on Intelligent Computing, Part II, 23-26 August, 2005, Hefei, China, pages 512-520. Springer-Verlag, 2005.

Digital Library

Google Scholar

[6]

Elke Michlmayr and Steve Cayzer. Learning user profiles from tagging data and leveraging them for personal (ized) information access. In Proceedings of the Workshop on Tagging and Metadata for Social Information Organization, co-located with the 16th International World Wide Web Conference (WWW2007), 8-12 May, 2007, Banff, Alberta, Canada, May 2007.

Google Scholar

[7]

Peter Mika. Ontologies are us: A unified model of social networks and semantics. Journal of Web Semantics, 5(1):5-15, 2007.

Digital Library

Google Scholar

[8]

M. E. J. Newman. Fast algorithm for detecting community structure in networks. Physical Review E, 69:066133, 2004.

Google Scholar

[9]

H. Sorensen and M. Mcelligot. Psun: A profiling system for usenet news. In CKIM'95 Workshop on Intelligent Information Agents, 1995.

Google Scholar

Cited By

View all

Zheng NLi Q(2018)A recommender system based on tag and time information for social tagging systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.09.13138:4(4575-4587)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.eswa.2010.09.131
Schöfegger KKörner CSinger PGranitzer MMunson EStrohmaier M(2012)Learning user characteristics from social tagging behaviorProceedings of the 23rd ACM conference on Hypertext and social media10.1145/2309996.2310031(207-212)Online publication date: 25-Jun-2012
https://dl.acm.org/doi/10.1145/2309996.2310031
Koutsonikola VVakali AGiannakidou EKompatsiaris I(2009)Clustering of Social Tagging System UsersProceedings of the 10th International Conference on Web Information Systems Engineering10.1007/978-3-642-04409-0_14(75-86)Online publication date: 13-Oct-2009
https://dl.acm.org/doi/10.1007/978-3-642-04409-0_14

Index Terms

Discovering and Modelling Multiple Interests of Users in Collaborative Tagging Systems

Recommendations

Tag Based Collaborative Filtering for Recommender Systems
RSKT '09: Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology

Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user's important personal preference information, it can be used to recommend personalized items to ...
Exploiting Social Tagging in a Web 2.0 Recommender System

Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take ...
Topic-based personalized recommendation for collaborative tagging system
HT '10: Proceedings of the 21st ACM conference on Hypertext and hypermedia

Collaborative tagging has become a very popular way to share, annotate, and discover online resources in Web 2.0. Yet as the number of resources in Collaborative tagging system grows over time, sifting through the large amounts of resources and finding ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03

December 2008

716 pages

ISBN:9780769534961

Publisher

IEEE Computer Society

United States

Publication History

Published: 09 December 2008

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
46
Total Downloads

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

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zheng NLi Q(2018)A recommender system based on tag and time information for social tagging systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.09.13138:4(4575-4587)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.eswa.2010.09.131
Schöfegger KKörner CSinger PGranitzer MMunson EStrohmaier M(2012)Learning user characteristics from social tagging behaviorProceedings of the 23rd ACM conference on Hypertext and social media10.1145/2309996.2310031(207-212)Online publication date: 25-Jun-2012
https://dl.acm.org/doi/10.1145/2309996.2310031
Koutsonikola VVakali AGiannakidou EKompatsiaris I(2009)Clustering of Social Tagging System UsersProceedings of the 10th International Conference on Web Information Systems Engineering10.1007/978-3-642-04409-0_14(75-86)Online publication date: 13-Oct-2009
https://dl.acm.org/doi/10.1007/978-3-642-04409-0_14

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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Tag Based Collaborative Filtering for Recommender Systems

Exploiting Social Tagging in a Web 2.0 Recommender System

Topic-based personalized recommendation for collaborative tagging system

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations