Opinion Dynamics-Based Group Recommender Systems

Castro, J; Lu, J; Zhang, G; Dong, Y; Martinez, L

Opinion Dynamics-Based Group Recommender Systems

Castro, J

Lu, J

Zhang, G

Dong, Y Martinez, L

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48 (12), pp. 2394 - 2406
Issue Date:: 2018-12-01

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Field	Value	Language
dc.contributor.author	Castro, J https://orcid.org/0000-0002-8390-8238	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	Dong, Y	en_US
dc.contributor.author	Martinez, L	en_US
dc.date.issued	2018-12-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48 (12), pp. 2394 - 2406	en_US
dc.identifier.issn	2168-2216	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132402
dc.description.abstract	© 2013 IEEE. With the accessibility to information, users often face the problem of selecting one item (a product or a service) from a huge search space. This problem is known as information overload. Recommender systems (RSs) personalize content to a user's interests to help them select the right item in information overload scenarios. Group RSs (GRSs) recommend items to a group of users. In GRSs, a recommendation is usually computed by a simple aggregation method for individual information. However, the aggregations are rigid and overlook certain group features, such as the relationships between the group members' preferences. In this paper, it is proposed a GRS based on opinion dynamics that considers these relationships using a smart weights matrix to drive the process. In some groups, opinions do not agree, hence the weights matrix is modified to reach a consensus value. The impact of ensuring agreed recommendations is evaluated through a set of experiments. Additionally, a sensitivity analysis studies its behavior. Compared to existing group recommendation models and frameworks, the proposal based on opinion dynamics would have the following advantages: 1) flexible aggregation method; 2) member relationships; and 3) agreed recommendations.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems	en_US
dc.relation.isbasedon	10.1109/TSMC.2017.2695158	en_US
dc.title	Opinion Dynamics-Based Group Recommender Systems	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	48	en_US
utslib.for	0803 Computer Software	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 Computer Science
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.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

© 2013 IEEE. With the accessibility to information, users often face the problem of selecting one item (a product or a service) from a huge search space. This problem is known as information overload. Recommender systems (RSs) personalize content to a user's interests to help them select the right item in information overload scenarios. Group RSs (GRSs) recommend items to a group of users. In GRSs, a recommendation is usually computed by a simple aggregation method for individual information. However, the aggregations are rigid and overlook certain group features, such as the relationships between the group members' preferences. In this paper, it is proposed a GRS based on opinion dynamics that considers these relationships using a smart weights matrix to drive the process. In some groups, opinions do not agree, hence the weights matrix is modified to reach a consensus value. The impact of ensuring agreed recommendations is evaluated through a set of experiments. Additionally, a sensitivity analysis studies its behavior. Compared to existing group recommendation models and frameworks, the proposal based on opinion dynamics would have the following advantages: 1) flexible aggregation method; 2) member relationships; and 3) agreed recommendations.

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http://hdl.handle.net/10453/132402