Product online multidimensional ratings aggregation decision-making model based on group division and attribute interaction

Abstract

The aim of this paper is to develop a novel approach to product online ratings aggregation decision-making, which can provide method support for consumers to obtain useful decision-making knowledge and more credible product ranking results. First, the personalized characteristics contained in the information disclosed by rating individual user are mined to establish the credibility model supporting individual weight allocation, and the multi-level division mechanism is designed to propose the aggregation method of group ratings. Then, the intuitionistic fuzzy improved normalized Bonferroni mean with weighted interaction (IFINWIBM) operator is defined, which can apply to aggregate product multidimensional ratings. Moreover, driven by expert knowledge and large-scale ratings, the learning mechanism of operator parameters is designed to describe the degree of interaction between attributes, which can avoid the unscientific caused by subjectivity. We further develop an online multidimensional ratings aggregation decision-making model to solve the product ranking problem. Finally, a numerical example and comparative analysis are given to illustrate the feasibility and advantages of the proposed method, which can reduce the interference of false groups on large-scale information aggregation and improve the rationality of attribute interaction coefficient acquisition.