Research on online review-trust model and synthetical value model based on power-law and density clustering algorithm

Abstract

The existing online shopping researches on consumer reviews are mostly based on the attitude change model (ACM). Although the ACM is valuable, it is not easy to judge the trustworthiness of the reviews and measure the values of the reviews. Based on the online data acquisition technology, we have got the data of 360buy, a domestic large-scale business to customer (B2C) commerce website in China. With application of data-mining and the density clustering algorithm (DBSCAN), we focus on the intervals distribution and the synthetic value of consumer reviews. The distribution of review interval can be depicted by the power-law function which presents a monotonically increasing relationship between the power-exponent and the customers’ concerns with the commodity: the higher the exponent is, the more attention will be drawn. We also find that the value of online reviews can be measured by the expertise value, which is the attraction and the quality of the reviews. Based on the above results, we have constructed the online review-trust model and the synthetic value model. The relationship between the power-exponent and the consumer attention has played a vital role in the consumer attention to online-shopping, and then the synthetic value model will help people find out useful reviews more effectively.