Minimum Cost Consensus Model Based on Granular Computing Through Online Reviews for Supporting Group Decision-Making

Recently, the minimum cost consensus model (MCCM) has received considerable attention in group decision-making. However, the prevailing trend in early MCCMs is mainly centered on model-centric approaches, which would produce unit adjustment cost and individual options. With the widespread adoption of information technologies and the availability of extensive datasets, accompanied by the advancement of machine learning, constructing MCCMs based on data-driven and machine learning methods, such as neural network (NN), is more practical and reliable. Additionally, considering the inherent variability in models and data, granular computing (GrC) and GrC-based NN can effectively resolve uncertainty. Therefore, this article presents an MCCM based on GrC and online reviews. First, the granular NN (GNN) is trained with online reviews to obtain decision-makers’ weights and unit adjustment costs based on the user information. Subsequently, through assessing the sentiment polarity of decision-makers’ reviews, their opinions are elicited and dynamically adjusted in the form of intervals. Furthermore, the bilateral granular consensus is defined to characterize the impact of decision-makers’ preferences on consensus from both sides. Finally, group decision-making on movies and television productions on douban.com is conducted using the proposed method. Sensitivity analysis demonstrates the flexibility of our consensus approach, and the comparative analysis shows its effectiveness.