Consumer credit evaluation model in C2C e-commerce using MCOC methods

DOI

10.2991/etmhs-18.2018.105How to use a DOI?

Keywords

E-commerce, multi-criteria optimization classifier, consumer credit evaluation, B2C,C2C.

Abstract

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2018 4th International Conference on Education Technology, Management and Humanities Science (ETMHS 2018)
PB  - Atlantis Press
SP  - 499
EP  - 502
SN  - 2352-5398
UR  - https://doi.org/10.2991/etmhs-18.2018.105
DO  - 10.2991/etmhs-18.2018.105
ID  - Chen2018/04
ER  -