Social network large-scale group decision-making (SN-LSGDM) has become an important research topic in the field of decision science. However, the current methods have some limitations: the trust network ignores the influence of opinions, the weights of decision-makers are subjective, and the consensus adjustment efficiency is low while ignoring individual differences. Therefore, this paper proposes a personalized consensus-reaching method for large-scale group decision-making based on similarity-corrected trust network. Firstly, a trust network correction method based on opinion similarity is constructed to adaptively optimize the initial trust relationship. Secondly, the entropy weight method is applied to realize the objective determination of decision maker weights based on opinion similarity and trust degree, avoiding the subjective bias caused by traditional preset coefficients. Thirdly, a reference matrix is determined based on the trust relationship, and differentiated personalized adjustment coefficients are designed, thereby forming an efficient personalized consensus reaching mechanism. Finally, the practicality of the proposed model is verified through an illustrative example of live-streaming e-commerce decision-making. In addition, simulation experiments and comparative analyses are conducted to highlight the superiority of the proposed model.
KeywordsLarge-Scale Group Decision Making (LSGDM)Social NetworkProbabilistic Linguistic Term Set (PLTS)Decision-Maker WeightsConsensus Reaching Process (CRP)
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