CBRM: Computational Reputation Model for C2C e-Commerce Platform

Online shopping has become an important and popular way of life. With the rapid C2C platform development, the credit problem in online shopping was being discussed. Serious loss of credit will leads to bad online business environment. However, till now lots of researchers just focus on seller reputation model to solve credit problem, thus buyer reputation model is seldom being considered. To solve the above problem, we proposed a Computational Buyer Reputation Model for C2C e-Commerce platform, i.e. CBRM. Five buyer reputation indicators, including transactions amount, seller ratings, buyer operations, chargeback ratio and real name authentication, were integrated into CBRM. By using AHP to distribute various indicator weights, we can obtain numeric value of buyer reputation. Therefore, these values can be used for sellers to choose appropriate buyers to deal with. Experiment results showed the efficiency and effectiveness of CBRM.