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

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.

Automated summary

In this article, we propose a continual personalized individual semantics learning model to support consensus-reaching in large-scale linguistic group decision making. The model derives personalized numerical scales from linguistic preference data, performs clustering ensemble method for group division and consensus management, and demonstrates its effectiveness through a case study on intelligent route optimization.