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- Publisher
- Association for Computing Machinery
- Copyright
- Copyright © 2023 Association for Computing Machinery.
- ISSN
- 1533-5399
- eISSN
- 1557-6051
- DOI
- 10.1145/3533432
- Publisher site
- See Article on Publisher Site
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.
Journal
ACM Transactions on Internet Technology (TOIT) – Association for Computing Machinery
Published: May 19, 2023
Keywords: Computing with words