Collaborative social group influence for event recommendation

Gao, L; Wu, J; Qiao, Z; Zhou, C; Yang, H; Hu, Y

Collaborative social group influence for event recommendation

Gao, L Wu, J Qiao, Z Zhou, C Yang, H

Hu, Y

Permalink

Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2016, 24-28-October-2016 pp. 1941 - 1944
Issue Date:: 2016-10-24

Closed Access

	Filename	Description	Size
	p1941-gao.pdf	Published version	315.42 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	Gao, L	en_US
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Qiao, Z	en_US
dc.contributor.author	Zhou, C	en_US
dc.contributor.author	Yang, H https://orcid.org/0000-0002-4328-335X	en_US
dc.contributor.author	Hu, Y	en_US
dc.date.issued	2016-10-24	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2016, 24-28-October-2016 pp. 1941 - 1944	en_US
dc.identifier.isbn	9781450340731	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121825
dc.description.abstract	© 2016 ACM. In event-based social networks, such as Meetup, social groups refer to self-organized communities that consist of users who share the same interests. In many real-world scenarios, users usually have social group preference and join interested social groups to attend events. It is therefore necessary to consider the influence of social groups to improve the event recommendation performance; however, existing event recommendation models generally consider users' individual preferences and neglect the influence of social groups. To this end, we propose a new Bayesian latent factor model SogBmf that combines social group influence and individual preference for event recommendation. Experiments on real-world data sets demonstrate the effectiveness of the proposed method.	en_US
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/2983323.2983879	en_US
dc.title	Collaborative social group influence for event recommendation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	24-28-October-2016	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	24-28-October-2016	en_US

Abstract:

© 2016 ACM. In event-based social networks, such as Meetup, social groups refer to self-organized communities that consist of users who share the same interests. In many real-world scenarios, users usually have social group preference and join interested social groups to attend events. It is therefore necessary to consider the influence of social groups to improve the event recommendation performance; however, existing event recommendation models generally consider users' individual preferences and neglect the influence of social groups. To this end, we propose a new Bayesian latent factor model SogBmf that combines social group influence and individual preference for event recommendation. Experiments on real-world data sets demonstrate the effectiveness of the proposed method.

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

http://hdl.handle.net/10453/121825