Rating Credits of Online Merchants Using Negative Ranks

Electronic commerce has become increasingly popular. Although it brings significant convenience to people's lives, a purchaser often hesitates to provide a negative rating to a merchant (or commodity) after a bad online shopping experience because his sensitive information (e.g., address, telephone number, and email address) would be available to the merchant. This makes the purchaser uncomfortable and even unsafe, and the purchaser tends to contribute better but untrue ratings. Hence, the privacy of the purchaser's score is vitally important to the development of electronic commerce. In this study, we apply for the first time an artificial immune technique (i.e., the negative survey) to rate the credits of online merchants, and we propose a new credit rating model, called a negative rating model, that could preserve the privacy of the purchaser's score with low additional computational cost. Moreover, the results obtained from our credit rating model exhibited a high correlation with those from the traditional evaluation model. Therefore, the proposed model has promising application prospects.