Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process

Dong, Y; Ran, Q; Chao, X; Li, C; Yu, S

Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process

Dong, Y Ran, Q Chao, X Li, C Yu, S

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Transactions on Internet Technology, 2023, 23, (2)
Issue Date:: 2023-05-19

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Field	Value	Language
dc.contributor.author	Dong, Y
dc.contributor.author	Ran, Q
dc.contributor.author	Chao, X
dc.contributor.author	Li, C
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-03-12T00:04:38Z
dc.date.available	2024-03-12T00:04:38Z
dc.date.issued	2023-05-19
dc.identifier.citation	ACM Transactions on Internet Technology, 2023, 23, (2)
dc.identifier.issn	1533-5399
dc.identifier.issn	1557-6051
dc.identifier.uri	http://hdl.handle.net/10453/176508
dc.description.abstract	When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	ACM Transactions on Internet Technology
dc.relation.isbasedon	10.1145/3533432
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	46 Information and computing sciences
dc.title	Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process
dc.type	Journal Article
utslib.citation.volume	23
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
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 - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-12T00:04:37Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	2

Abstract:

When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.

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http://hdl.handle.net/10453/176508