On group extraction and fusion for tag-based social recommendation

Xu, G; Gu, Y; Yi, X

On group extraction and fusion for tag-based social recommendation

Xu, G

Gu, Y Yi, X

Permalink

Publication Type:: Chapter
Citation:: Social Media Mining and Social Network Analysis: Emerging Research, 2013, pp. 211 - 223
Issue Date:: 2013-12-01

Closed Access

	Filename	Description	Size
	2012001004OK.pdf		1.84 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Gu, Y	en_US
dc.contributor.author	Yi, X	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Social Media Mining and Social Network Analysis: Emerging Research, 2013, pp. 211 - 223	en_US
dc.identifier.isbn	9781466628069	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26292
dc.identifier.uri	http://hdl.handle.net/10453/35448
dc.description.abstract	With the recent information explosion, social websites have become popular in many Web 2.0 applications where social annotation services allow users to annotate various resources with freely chosen words, i.e., tags, which can facilitate users' finding preferred resources. However, obtaining the proper relationship among user, resource, and tag is still a challenge in social annotation-based recommendation researches. In this chapter, the authors aim to utilize the affinity relationship between tags and resources and between tags and users to extract group information. The key idea is to obtain the implicit relationship groups among users, resources, and tags and then fuse them to generate recommendation. The authors experimentally demonstrate that their strategy outperforms the state-of-the-art algorithms that fail to consider the latent relationships among tagging data. © 2013, IGI Global.	en_US
dc.relation.ispartof	Social Media Mining and Social Network Analysis: Emerging Research	en_US
dc.relation.isbasedon	10.4018/978-1-4666-2806-9.ch014	en_US
dc.title	On group extraction and fusion for tag-based social recommendation	en_US
dc.type	Chapter
utslib.for	080109 Pattern Recognition and Data Mining	en_US
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:

With the recent information explosion, social websites have become popular in many Web 2.0 applications where social annotation services allow users to annotate various resources with freely chosen words, i.e., tags, which can facilitate users' finding preferred resources. However, obtaining the proper relationship among user, resource, and tag is still a challenge in social annotation-based recommendation researches. In this chapter, the authors aim to utilize the affinity relationship between tags and resources and between tags and users to extract group information. The key idea is to obtain the implicit relationship groups among users, resources, and tags and then fuse them to generate recommendation. The authors experimentally demonstrate that their strategy outperforms the state-of-the-art algorithms that fail to consider the latent relationships among tagging data. © 2013, IGI Global.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26292