Consumer credit evaluation model in C2C e-commerce using MCOC methods
Shuang Chen, Hong-Yun Gao, Dan Li, Fan-Yun Meng
Available Online April 2018.
- https://doi.org/10.2991/etmhs-18.2018.105How to use a DOI?
- E-commerce, multi-criteria optimization classifier, consumer credit evaluation, B2C,C2C.
- In this paper, we investigate a method named multi-criteria optimization classifier (MCOC)to hedge consumer credit evaluation in consumer-to-consumer (C2C) e-commerce. Consumer credit is one of the key obstacles to vendors succeeding on the internet medium and a lack of consumer credit is likely to discourage online businesses from participating in e-commerce. Our experimental results of consumer credit evaluation based on data sets from TaoBao show that MCOC can enhance the separation of different consumers, the efficiency of credit evaluation, and the generalization of predicting the credit rank of a new consumer.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Shuang Chen AU - Hong-Yun Gao AU - Dan Li AU - Fan-Yun Meng PY - 2018/04 DA - 2018/04 TI - Consumer credit evaluation model in C2C e-commerce using MCOC methods BT - 2018 4th International Conference on Education Technology, Management and Humanities Science (ETMHS 2018) PB - Atlantis Press SN - 2352-5398 UR - https://doi.org/10.2991/etmhs-18.2018.105 DO - https://doi.org/10.2991/etmhs-18.2018.105 ID - Chen2018/04 ER -