A Large-Scale Reviews-Driven Multi-Criteria Product Ranking Approach Based on User Credibility and Division Mechanism

Abstract

Massive online reviews provide consumers with the convenience of obtaining product information, but it is still worth exploring how to provide consumers with useful and reliable product rankings. The existing ranking methods do not fully mine user information, rating, and text comment information to obtain scientific and reasonable information aggregation methods. Therefore, this study constructs a user credibility model and proposes a large-scale user information aggregation method to obtain a new product ranking method. First, in order to obtain the aggregate weight of large-scale users, this paper proposes a consistency modeling method of text comments and star ratings by mining the associated information of user comments, including user interaction information and user personalized characteristics information, combined with sentiment analysis technology, and then constructs a user credibility model. Second, a double-layer group division mechanism considering user regions and comment time is designed to develop the large-scale group ratings aggregation approach. Third, based on the user credibility model and the large-scale ratings aggregation approach, a product ranking method is developed. Finally, the feasibility and effectiveness of the proposed method are verified through a case study for automobile ranking and a comparative analysis is furnished. The analysis results of the application case of automobile ranking show that there is a significant difference between the ranking results obtained by the ratings aggregation method based on the arithmetic mean and the ranking results obtained by this method. The method in this study comprehensively considers user credibility and group division, which can be reflected in user aggregation weights and the group aggregation process, and can also obtain more scientific and reasonable decision results.