Hybridizing Social Filtering for Recommender Systems

Mao, M; Lu, J; Zhang, G; Zhang, J

Hybridizing Social Filtering for Recommender Systems

Mao, M Lu, J

Zhang, G

Zhang, J

Permalink

Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2014, 277 pp. 273 - 285
Issue Date:: 2014-01-01

Closed Access

	Filename	Description	Size
	paper.pdf	Accepted Manuscript version	494.11 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	Mao, M	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Zhang, J	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2014, 277 pp. 273 - 285	en_US
dc.identifier.isbn	9783642549236	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35437
dc.description.abstract	Users send requests to recommender systems for getting suggested products or services. Collaborative filtering is a popular technique for making such suggestions efficiently, but it suffers from a drawback known as "cold-start" problem. Social filtering may succeed for such users, since it utilize the extra social relations of users. It gives us opportunities to eliminate the limitations by hybridizing social filtering into traditional collaborative filtering. To handle this issue, differing from previous fusion models that only combine the final results, this paper proposed a new neighborhood fusion model to make hybridization at an earlier and deeper stage. Experiment-based comparative analyses are also conducted. The results show that our model is of a higher recommendation quality, on different datasets. © Springer-Verlag Berlin Heidelberg 2014.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-642-54924-3_26	en_US
dc.title	Hybridizing Social Filtering for Recommender Systems	en_US
dc.type	Conference Proceeding
utslib.citation.volume	277	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
dc.location.activity	Shenzhen, 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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	277	en_US

Abstract:

Users send requests to recommender systems for getting suggested products or services. Collaborative filtering is a popular technique for making such suggestions efficiently, but it suffers from a drawback known as "cold-start" problem. Social filtering may succeed for such users, since it utilize the extra social relations of users. It gives us opportunities to eliminate the limitations by hybridizing social filtering into traditional collaborative filtering. To handle this issue, differing from previous fusion models that only combine the final results, this paper proposed a new neighborhood fusion model to make hybridization at an earlier and deeper stage. Experiment-based comparative analyses are also conducted. The results show that our model is of a higher recommendation quality, on different datasets. © Springer-Verlag Berlin Heidelberg 2014.

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

http://hdl.handle.net/10453/35437