Group division for recommendation in tag-based systems

Pan, R; Xu, G; Dolog, P; Zong, Y

Group division for recommendation in tag-based systems

Pan, R Xu, G

Dolog, P Zong, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012, 2012, pp. 399 - 404
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Pan, R	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Dolog, P	en_US
dc.contributor.author	Zong, Y	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012, 2012, pp. 399 - 404	en_US
dc.identifier.isbn	9780769548647	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31079
dc.description.abstract	The common usage of tags in these systems is to add the tagging attribute as an additional feature to re-model users or resources over the tag vector space, and in turn, making tag-based recommendation or personalized recommendation. With the help of tagging data, user annotation preference and document topical tendency are substantially coded into the profiles of users or documents. However, obtaining the proper relationship among user, resource and tag is still a challenge in social annotation based recommendation researches. In this paper, we utilize the relationship from between tags and resources and between tags and users to extract group information. With the help of such relationship, we can obtain the Topic-Groups based on the bipartite relationship between tags and resources, and Interest-Groups based on the bipartite relationship between tags and users. The preliminary experiments have been conducted on Movie Lens dataset to compare our proposed approach with the traditional collaborative filtering recommendation approach approach in terms of precision, and the result demonstrates that our approach could considerably improve the performance of recommendations. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012	en_US
dc.relation.isbasedon	10.1109/CGC.2012.124	en_US
dc.title	Group division for recommendation in tag-based systems	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Xiangtan, China
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

The common usage of tags in these systems is to add the tagging attribute as an additional feature to re-model users or resources over the tag vector space, and in turn, making tag-based recommendation or personalized recommendation. With the help of tagging data, user annotation preference and document topical tendency are substantially coded into the profiles of users or documents. However, obtaining the proper relationship among user, resource and tag is still a challenge in social annotation based recommendation researches. In this paper, we utilize the relationship from between tags and resources and between tags and users to extract group information. With the help of such relationship, we can obtain the Topic-Groups based on the bipartite relationship between tags and resources, and Interest-Groups based on the bipartite relationship between tags and users. The preliminary experiments have been conducted on Movie Lens dataset to compare our proposed approach with the traditional collaborative filtering recommendation approach approach in terms of precision, and the result demonstrates that our approach could considerably improve the performance of recommendations. © 2012 IEEE.

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http://hdl.handle.net/10453/31079