Social friend recommendation based on network correlation and feature co-clustering

Huang, S; Zhang, J; Lu, S; Hua, XS

Social friend recommendation based on network correlation and feature co-clustering

Huang, S Zhang, J

Lu, S Hua, XS

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Publication Type:: Conference Proceeding
Citation:: ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval, 2015, pp. 315 - 322
Issue Date:: 2015-06-22

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Field	Value	Language
dc.contributor.author	Huang, S	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Lu, S	en_US
dc.contributor.author	Hua, XS	en_US
dc.date.available	2015-03-16	en_US
dc.date.issued	2015-06-22	en_US
dc.identifier.citation	ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval, 2015, pp. 315 - 322	en_US
dc.identifier.isbn	9781450332743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121628
dc.description.abstract	Copyright © 2015 ACM. Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, friend recommendation is a difficult task and most social websites use simple friend recommendation algorithms such as similarity and popularity, whose level of accuracy does do not satisfy the majority of users. In this paper we propose a two-stage procedure for more accurate friend recommendation: In the first stage, based on the relationship of different social networks, the Flickr tag network and contact network are aligned to generate a "possible friend list"; In the second stage, making the assumption that "a friend's friends also tend to be friends", co-clustering is applied to the tag and image information of the list to refine the recommendation result in the first stage. Experimental results show that the proposed method achieves good performance and every stage contributes to the recommendation.	en_US
dc.relation.ispartof	ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval	en_US
dc.relation.isbasedon	10.1145/2671188.2749325	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Social friend recommendation based on network correlation and feature co-clustering	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

Copyright © 2015 ACM. Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, friend recommendation is a difficult task and most social websites use simple friend recommendation algorithms such as similarity and popularity, whose level of accuracy does do not satisfy the majority of users. In this paper we propose a two-stage procedure for more accurate friend recommendation: In the first stage, based on the relationship of different social networks, the Flickr tag network and contact network are aligned to generate a "possible friend list"; In the second stage, making the assumption that "a friend's friends also tend to be friends", co-clustering is applied to the tag and image information of the list to refine the recommendation result in the first stage. Experimental results show that the proposed method achieves good performance and every stage contributes to the recommendation.

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

http://hdl.handle.net/10453/121628