User Review based Rating for E-Commerce Platform
Wei Wang, Chunxi Li, Yongxiang Zhao, Xin Meng, Baoxian Zhang · 2019
A typical e-commerce platform has too many similar items for sale, making it difficult for customers to choose from. User reviews left by previous buyers are worth reading to help customers make purchase choices. However, due to the large number of reviews, users cannot read all reviews to extract real useful information. In this paper, we propose a user review driven rating system, which is particularly designed for Tmall, a famous Chinese e-commerce platform, to help customers to understand the differences among similar items for finding a satisfactory one. Numerical result demonstrates that, on average, aggregated score calculated by our rating system for items is as efficient as the score given by Tmall, while our rating can differentiate items at much finer granularity by the computed multi-dimension scores.