Consumer evaluation mechanisms on e-commerce platforms: reputation management and analysis of influencing factors

Abstract

This paper proposes a regression algorithm to statistically relate the relationship between the relationship variables and proposes a multiple linear regression model to conduct a linear correlation study between a dependent variable and multiple independent variables. The factors that affect the value of merchant reputation in the content of consumer evaluation are regressed and analyzed, and a model for evaluating merchant reputation is proposed based on the regression results. In the merchant reputation evaluation model, the Bayesian algorithm and multi-source data fusion method are used to calculate the merchant weight, the Euclidean distance is used to calculate the platform weight, and the judgment matrix is constructed to calculate the merchant e-commerce reputation score. In the model performance test, 10 Taobao merchants are randomly selected for reputation calculation. The highest merchant reputation scores of 4.582 and 4.584 can be obtained for merchants S9 and S10, and the index scores are more accurate. The original Taobao reputation scores of merchants S4, S8, and S9 are all 4.97. At the same time, the model in this paper calculates them to be 4.404, 4.304, and 4.582, respectively, which are more reflective of the real reputation level of merchants.

Keywords

• multiple linear regression
• multi-source data fusion
• bayesian algorithm
• merchant reputation
• 68-02