Study on Credit Evaluation Model and Algorithm for C2C E-Commerce

Piao, Chunhui; J, An; Fang, Meiqi

doi:10.1109/icebe.2007.77

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…In recent years, some researchers have paid considerable attention to the credit score problem in e-commerce, especially for the C2C e-commerce type [19][20][21]. We noted that these studies always focused on transaction data, while ignoring the data on other elements of the cross-border trade chain.…”

Section: Related Workmentioning

confidence: 99%

Toward an Efficient and Effective Credit Scorer for Cross-Border E-Commerce Enterprises

Guo

Zhang

et al. 2023

Scientific Programming

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Building an efficient and effective credit scorer for enterprises is an important and urgent demand in the cross-border e-commerce industry. In this paper, we present a framework to build a credit scorer using e-commerce data integrated from various sources. First, an improved dependency graph approach is proposed to recognize distinct records in the dataset. Then, we apply logistic regression using a prejudice remover regularizer to train the model, preceded by predictor preparation through binning and evaluating their information value. Lastly, we build the credit scorer according to the coefficients of the model. We implement our framework on a dataset from the official customs database and a large cross-border e-commerce platform. The empirical results demonstrate that the scorer built by our methodology can be used to effectively evaluate enterprises, while also removing prejudice against small and medium enterprises to a certain extent.

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Section: Related Workmentioning

confidence: 99%

Toward an Efficient and Effective Credit Scorer for Cross-Border E-Commerce Enterprises

Guo

Zhang

et al. 2023

Scientific Programming

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“…Piao C.H. et al (2007) [7] improved the existing C2C credit evaluation model and established a new credit evaluation model, proposed a new algorithm, calculated the weighted average credit score and credit degree of the evaluated user by weighing and considering the credit degree, transaction times and transaction amount of the trading party, and then determined the credit rating of the evaluated user. Liu Z.F.…”

Section: Development Trend Analysismentioning

confidence: 99%

Research Hotspots and Trends of E-Commerce Credit Evaluation in China Based on CiteSpace

Bu¹,

Zhang²,

Zhang³

2020

Proceedings of the 2020 4th International Seminar on Education, Management and Social Sciences (ISEMSS 2020)

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【Significance】With the rapid development of e-commerce in the era of big data, its credit evaluation becomes more and more important. This paper combs and studies the existing research literature in the field of e-commerce credit evaluation, so as to achieve a comprehensive understanding of the follow-up research hotspots and trends in this field and provide ideas for the development of follow-up research.【Process】(1) Using the Database of CNKI as the sample source, CiteSpace visual analysis software was used to draw the collaborative network map of high-yield authors, cooperative network map of important institutions, cooccurrence network map of high-frequency keywords and the timezone map of recent five years of research.(2) In order to better understand this field, literature analysis method was used to conduct deep learning research on the papers with high citation and high quality in the sample data. 【Conclusion】At the present stage, the research in this field is in the bottleneck period. The research hotspots are mainly divided into three aspects: research on C2C e-commerce platform, research on evaluation system, and research on model establishment. The frontier trends mainly include the research under the background of big data, the system improvement of e-commerce Law, and the application of machine learning algorithm.

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“…For example, a seller got 100 "good", while another seller got 200 "good", 100 "bad". In this case, both the seller's credit situation should be different, even vary widely, but the credit accumulative total value is the same, this model is difficult to reflect the seller's real credit value [4,5]. At the same time, there is no account of the transaction amount, which has led users to credit hype [6]; there is no consider of the validity of transaction time, resulting in new and old seller cannot compete in a fair trading environment, make the new one tend to grow slowly due to the beginning of a low credit score, which maybe prompt new users credit hype to improve their credit score.…”

Section: The Existing Accumulation Modelmentioning

confidence: 99%

The Improved C2C E-Commerce Credit Model ----A Case Study of Taobao

He¹

2015

Proceedings of the 2015 International Conference on Engineering Management, Engineering Education and Information Technology

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E-commerce has developed rapidly in recent years; the credit problems bring a lot of trouble to consumers. This paper has taken taobao as an example, found out the existing problems, and improved the index system by removing the logistics indicators and taking transaction time and transaction amount into consideration and using weight analysis with AHP method to establish a new credit model. Finally, we have made an empirical analysis by comparing the existing evaluation model and the improved model, the result shows that our new model is more efficient and practical.

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Study on Credit Evaluation Model and Algorithm for C2C E-Commerce

Cited by 8 publications

References 3 publications

Toward an Efficient and Effective Credit Scorer for Cross-Border E-Commerce Enterprises

Toward an Efficient and Effective Credit Scorer for Cross-Border E-Commerce Enterprises

Research Hotspots and Trends of E-Commerce Credit Evaluation in China Based on CiteSpace

The Improved C2C E-Commerce Credit Model ----A Case Study of Taobao

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