A User Purchase Behavior Prediction Method Based on XGBoost

Author:

Wang Wenle1ORCID,Xiong Wentao1,Wang Jing1,Tao Lei2,Li Shan3,Yi Yugen1ORCID,Zou Xiang1,Li Cui4

Affiliation:

1. School of Software, Jiangxi Normal University, Nanchang 330022, China

2. Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong 999077, China

3. School of Economics and Management, East China Jiaotong University, Nanchang 330013, China

4. School of Intercultural Studies, Jiangxi Normal University, Nanchang 330022, China

Abstract

With the increasing use of electronic commerce, online purchasing users have been rapidly rising. Predicting user behavior has therefore become a vital issue based on the collected data. However, traditional machine learning algorithms for prediction require significant computing time and often produce unsatisfactory results. In this paper, a prediction model based on XGBoost is proposed to predict user purchase behavior. Firstly, a user value model (LDTD) utilizing multi-feature fusion is proposed to differentiate between user types based on the available user account data. The multi-feature behavior fusion is carried out to generate the user tag feature according to user behavior patterns. Next, the XGBoost feature importance model is employed to analyze multi-dimensional features and identify the model with the most significant weight value as the key feature for constructing the model. This feature, together with other user features, is then used for prediction via the XGBoost model. Compared to existing machine learning models such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Back Propagation Neural Network (BPNN), the eXtreme Gradient Boosting (XGBoost) model outperforms with an accuracy of 0.9761, an F1 score of 0.9763, and a ROC value of 0.9768. Thus, the XGBoost model demonstrates superior stability and algorithm efficiency, making it an ideal choice for predicting user purchase behavior with high levels of accuracy.

Funder

National Natural Science Foundation of China

National Social Science Foundation

Social Science Planning Project in Jiangxi Province

Science and Technology Research Project of the Jiangxi Provincial Department of Education

NSFC

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Reference36 articles.

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2. Wu, H.T.A. (2021, January 28–30). Prediction Method of User Purchase Behavior Based on Bidirectional Long Short-Term Memory Neural Network Model. Proceedings of the 2021 2nd International Conference on Artificial Intelligence and Information Systems, Chongqing, China.

3. Internet of Things in the 5G Era: Enablers, Architecture, and Business Models;Palattella;IEEE J. Sel. Areas Commun.,2016

4. Yao, Y.H., Yen, B., and Yip, A. (2015, January 6–10). Examining the Effects of the Internet of Things (IoT) on E-Commerce: Alibaba Case Study. Proceedings of the 15th International Conference on Electronic Business (ICEB 2015), Hong Kong, China.

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