In linguistic large-scale group decision making (LSGDM), it is often necessary to achieve a consensus. Particularly, when computing with words and linguistic decision, we must keep in mind that words mean different things to different people. Therefore, to represent the specific semantics of each individual, we need to consider the personalized individual semantics (PIS) model in linguistic LSGDM. In this paper, we propose a consensus model based on PIS for LSGDM. Specifically, a PIS process to obtain the individual semantics of linguistic terms with linguistic preference relations is introduced. A consensus process based on PIS, including the consensus measure and feedback recommendation phases, is proposed to improve the willingness of decision makers who follow the suggestions to revise their preferences in order to achieve a consensus in linguistic LSGDM problems. The consensus measure defines two opposing consensus groups with respective acceptable and unacceptable consensus. In the feedback recommendation phase, a PIS-based clustering method to get decision makers with similar individual semantics is proposed. Recommendation rules design a feedback for decision makers with unacceptable consensus, finding suitable moderators from the decision makers with acceptable consensus based on cluster proximity.

中文翻译：

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基于聚类个性化个体语义和对立共识群体的反馈推荐大规模语言群体决策共识模型


在语言大规模群体决策（LSGDM）中，通常需要达成共识。特别是，当使用单词和语言决策进行计算时，我们必须记住，单词对不同的人来说意味着不同的东西。因此，为了表示每个个体的具体语义，我们需要考虑语言LSGDM中的个性化个体语义（PIS）模型。在本文中，我们提出了一种基于 PIS 的 LSGDM 共识模型。具体来说，引入了获取具有语言偏好关系的语言术语的个体语义的PIS过程。提出了一种基于PIS的共识过程，包括共识测量和反馈推荐阶段，以提高决策者遵循建议修改偏好的意愿，从而在语言LSGDM问题上达成共识。共识度量定义了两个对立的共识组，分别具有可接受和不可接受的共识。在反馈推荐阶段，提出了一种基于PIS的聚类方法来获取具有相似个体语义的决策者。推荐规则为共识不可接受的决策者设计反馈，根据集群邻近度从共识可接受的决策者中寻找合适的调节者。