Dissecting click farming on the Taobao platform in China via PU learning and weighted logistic regression-Reference-Cited by-同舟云学术

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Dissecting click farming on the Taobao platform in China via PU learning and weighted logistic regression

Published:2020-06-02 Issue: Volume: Page:
ISSN:1389-5753
Container-title:Electronic Commerce Research
language:en
Short-container-title:Electron Commer Res

Author:

Jiang Cuixia,Zhu Jun,Xu Qifa^ORCID

Funder

National Natural Science Foundation of China

Humanity and Social Science Foundation of the Ministry of Education of China

National Statistical Science Research Projects of China

Publisher

Springer Science and Business Media LLC

Subject

Human-Computer Interaction,Economics, Econometrics and Finance (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s10660-020-09418-z.pdf

Reference47 articles.

1. Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134–142.

2. Barabesi, L., Cerasa, A., Perrotta, D., & Cerioli, A. (2016). Modeling international trade data with the Tweedie distribution for anti-fraud and policy support. European Journal of Operational Research, 248(3), 1031–1043.

3. Berrar, D. (2016). Learning from automatically labeled data: Case study on click fraud prediction. Knowledge and Information Systems, 46, 477–490.

4. de Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Redondo-Expósito, L. (2018). Positive unlabeled learning for building recommender systems in a parliamentary setting. Information Sciences, 433, 221–232.

5. Carneiro, N., Figueira, G., & Costa, M. (2017). A data mining based system for credit-card fraud detection in e-tail. Decision Support Systems, 95, 91–101.

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