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- W4229027091 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." @default.
- W4229027091 created "2022-05-08" @default.
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- W4229027091 date "2023-05-19" @default.
- W4229027091 modified "2023-09-27" @default.
- W4229027091 title "Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process" @default.
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- W4229027091 doi "https://doi.org/10.1145/3533432" @default.
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