Superiority-Probabilistic-Based Products Ranking Method: Integrating Users' Consensus Experiences and Consumers' Individual Preferences

Abstract

In the information edge, online reviews have emerged as a pivotal factor in consumers' purchasing decisions. In reality, we are accustomed to obtaining rigid product rankings from online reviews. However, online reviews naturally contain varied and sometimes conflicting viewpoints; the rigid ranking results are not completely reasonable. To address this problem, the paper proposes a superiority-probabilistic-based ranking model by integrating both users' consensus experiences and consumers' individual preferences. Firstly, the users' experiences, primarily represented by star ratings and textual reviews, are transformed into the individual evaluation domains (IEDs) through sentiment analysis. Then, the IEDs are further fused into the comprehensive evaluation domain (CED) by highlighting the users' consensus experiences. Subsequently, an individual-preference-oriented indicator weighting method is introduced by referring to the sequential relationship analysis (SRA) method. Based on the above preparations, the superiority-probability-based pairwise comparison matrix (SPM) is calculated through a thorough sampling of CED and the iterative aggregation of it with the indicator weights. The SPM reflects the relative superiority probability among any products, from which the probabilistic ranking of products can be derived. The case study illustrates the validity and practicability of the proposed methods. The research contribution establishes a superiority-probabilistic-based products ranking framework, providing consumers with more detailed purchase recommendations.