Prediction of purchase intention among e-commerce platform users based on big data analysis

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Abstract

The boom of e-commerce platforms (ECPs) has created a massive amount of data on user behaviors. To realize precision marketing, the ECPs must mine out the effective information from the massive data, and predict the purchase intention of their users. Therefore, this paper attempts to design an effective prediction model of purchase intention among ECP users. Firstly, feature engineering, coupled with big data analysis, was performed to identify the features that directly bear on the purchase intention of ECP users. Drawing on these features, two prediction models were established based on linear regression (LR) and extreme gradient boosting (XGBoost), respectively. The XGBoost model was found to be more effective through experiment on ECP users using cellphones. Finally, the prediction effects of the XGBoost-based prediction model were verified through an experiment on Epinions Trust Network Dataset. The research results provide new insights into user behaviors on ECPs.

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APA

Guo, Q., Yang, C., & Tian, S. (2020). Prediction of purchase intention among e-commerce platform users based on big data analysis. Revue d’Intelligence Artificielle, 34(1), 95–100. https://doi.org/10.18280/ria.340113

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