Improving Group Recommendations by Identifying Homogenous Subgroups

Komkhao, M; Lu, J; Li, Z; Halang, WA

Improving Group Recommendations by Identifying Homogenous Subgroups

Komkhao, M Lu, J

Li, Z Halang, WA

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2014, 214 pp. 453 - 462
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Komkhao, M	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Li, Z	en_US
dc.contributor.author	Halang, WA	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2014, 214 pp. 453 - 462	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37419
dc.description.abstract	Recommender systems have proven their effectiveness in supporting personalised purchasing decisions and e-service intelligence. In order to support members in user groups of recommender systems, recently designed group recommender systems search for data relevant to all group members and discover the agreements between members of online communities. This paper focuses on achieving common satisfaction for groups or communities by, e.g. finding a restaurant for a family or shoes for a group of cheerleaders. It establishes an algorithm, called I-GRS, to devise group recommender systems based on incremental model-based collaborative filtering and applying the Mahalanobis distance and fuzzy membership to create groups of users with similar interests. Finally, an algorithm and related design strategy to build group recommender systems is proposed. A set of experiments is set up to evaluate the performance of the I-GRS algorithm in group recommendations. The results show its effectiveness vis-à-vis the recommendations made by classical recommender systems to single or groups of individuals. © Springer-Verlag Berlin Heidelberg 2014.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-642-37832-4_41	en_US
dc.title	Improving Group Recommendations by Identifying Homogenous Subgroups	en_US
dc.type	Conference Proceeding
utslib.citation.volume	214	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Beijing, 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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	214	en_US

Abstract:

Recommender systems have proven their effectiveness in supporting personalised purchasing decisions and e-service intelligence. In order to support members in user groups of recommender systems, recently designed group recommender systems search for data relevant to all group members and discover the agreements between members of online communities. This paper focuses on achieving common satisfaction for groups or communities by, e.g. finding a restaurant for a family or shoes for a group of cheerleaders. It establishes an algorithm, called I-GRS, to devise group recommender systems based on incremental model-based collaborative filtering and applying the Mahalanobis distance and fuzzy membership to create groups of users with similar interests. Finally, an algorithm and related design strategy to build group recommender systems is proposed. A set of experiments is set up to evaluate the performance of the I-GRS algorithm in group recommendations. The results show its effectiveness vis-à-vis the recommendations made by classical recommender systems to single or groups of individuals. © Springer-Verlag Berlin Heidelberg 2014.

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http://hdl.handle.net/10453/37419