Discovering user interests from social images

Yao, J; Zhang, Y; Tsang, I; Sun, J

Discovering user interests from social images

Yao, J Zhang, Y Tsang, I

Sun, J

Permalink

Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10133 LNCS pp. 160 - 172
Issue Date:: 2017-01-01

Closed Access

	Filename	Description	Size
	MMM.pdf	Accepted Manuscript version	779.08 kB	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	Yao, J	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Tsang, I https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Sun, J	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10133 LNCS pp. 160 - 172	en_US
dc.identifier.isbn	9783319518138	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127684
dc.description.abstract	© Springer International Publishing AG 2017. The last decades have witnessed the boom of social networks. As a result, discovering user interests from social media has gained increasing attention. While the accumulation of social media presents us great opportunities for a better understanding of the users, the challenge lies in how to build a uniform model for the heterogeneous contents. In this article, we propose a hybrid mixture model for user interests discovery which exploits both the textual and visual content associated with social images. By modeling the features of each content source independently at the latent variable level and unifies them as latent interests, the proposed model allows the semantic interpretation of user interests in both the visual and textual perspectives. Qualitative and quantitative experiments on a Flickr dataset with 2.54 million images have demonstrated its promise for user interest analysis compared with existing methods.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-51814-5_14	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Discovering user interests from social images	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10133 LNCS	en_US
utslib.for	0806 Information Systems	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	10133 LNCS	en_US

Abstract:

© Springer International Publishing AG 2017. The last decades have witnessed the boom of social networks. As a result, discovering user interests from social media has gained increasing attention. While the accumulation of social media presents us great opportunities for a better understanding of the users, the challenge lies in how to build a uniform model for the heterogeneous contents. In this article, we propose a hybrid mixture model for user interests discovery which exploits both the textual and visual content associated with social images. By modeling the features of each content source independently at the latent variable level and unifies them as latent interests, the proposed model allows the semantic interpretation of user interests in both the visual and textual perspectives. Qualitative and quantitative experiments on a Flickr dataset with 2.54 million images have demonstrated its promise for user interest analysis compared with existing methods.

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

http://hdl.handle.net/10453/127684