Online Service Platform Product Ranking: An Interval Basic Uncertain Linguistic Information OWA Operator-Based MCGDM Model With Large-Scale Consumer Ratings

The proliferation of ‘emerging positive reviews’ poses a pressing challenge for online service platform products, necessitating the incorporation of rating credibility into product ranking decisions. To address this issue, we propose a large-scale ratings aggregation-driven approach for product ranking, leveraging basic uncertain linguistic information (BULI) that can simultaneously characterize and evaluate information along with its credibility. Our study is grounded in the development of novel operators tailored for linguistic information environments. Firstly, we introduce an extended t-norm and t-conorm ordered weighted averaging (ESTOWA) operator, followed by the extended BULI ordered weighted averaging (EBUIOWA) operator. Secondly, we propose the BULI ordered weighted averaging (BULIOWA) operator and interval BULI (IBULI) ordered weighted averaging (IBULIOWA) operator for aggregating IBULI pairs. We also thoroughly investigate the monotonicity and degeneracy characteristics of these operators. Furthermore, we develop a method to convert ratings into IBULI pairs, considering user credibility and regional characteristics of product ratings. Additionally, we propose a dynamic PROMETHEE-II model in an IBULI environment, taking into account the temporal characteristics of user ratings. And we develop a multi-criteria group decision making model for online service platform product ranking. Finally, we validate the effectiveness and applicability of our approach through a case study on product ranking in an online automotive service platform, thereby demonstrating its efficacy and practicality.