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

Published:19 May 2023Publication History
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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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    • Published in

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 23, Issue 2
      May 2023
      276 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3597634
      • Editor:
      • Ling Liu
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Publication History

      • Published: 19 May 2023
      • Online AM: 6 May 2022
      • Accepted: 24 April 2022
      • Revised: 24 November 2021
      • Received: 30 April 2021
      Published in toit Volume 23, Issue 2

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