Study On The Seller’s Credit Evaluation In C2C E-Commerce Based On Recommendation

With the popularization of Internet and e-commerce, more and more people begin to accept and choose online shopping, in which C2C e-commerce has become more popular among individual users, because of the low barriers to entry and the simple transaction process. However, the rapid increase of information and the number of registered users, even more and more problems about credit, this situation make it hard for new consumers to find the information about products and sellers, and even hard to establish a good trust relationship. The trust is the premise for online transactions between people who don’t know each other before. So the credit evaluation in C2C e-commerce is so important, which can reflect the seller’s trust degree through other consumers who have had transaction with it. The result of evaluation is useful for new consumer to make decision and make a better trust relationship with seller before transaction.This paper studies the seller credit evaluation of C2C e-commerce. The main work is summarized as follows:(1) Review the concept about credit, trust, credit evaluation of e-commerce, the method used in credit evaluation in e-commerce and the recent research on credit evaluation of C2C e-commerce. Based on this propose the main research problems in this paper and make clear of the paper’s research target, content, method and idea.(2) Introduce the measures of credit management in C2C website, analyze the common credit problems and the problems of credit evaluation in credit management of C2C website.(3) Analyze the influencing factors of seller’s credit, based on these analyses an index system is set up to evaluate seller’s credit by virtue of questionnaire investigation and statistical analysis.(4) Analyze the basic thought about trust based on recommendation in C2C e-commerce, and use it for seller’s credit evaluation, including identify user for recommendation and credit evaluation. So it can give support for new buyer to choose seller.(5) One method is provided to identify user for recommendation, namely user identify method based on fuzzy clustering. Two methods are provided to evaluate seller’s credit, namely credit evaluation method based on linguistic operators and credit evaluation method based on D-S evidence theory. Then, the corresponding examples are given based on the index system, and further more the application of these methods are explained.