User behavior prediction based on machine learning

Luyao Shan; Honghao Liu; Ruoxi Wang

doi:10.1117/12.2628793

7 March 2022 User behavior prediction based on machine learning

Luyao Shan, Honghao Liu, Ruoxi Wang

Author Affiliations +

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121672K (2022) https://doi.org/10.1117/12.2628793
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

In the age of big data, online shopping is now overgrowing and becoming an emerging trend among customers. Understanding user behaviors can let e-commerce platforms identify target customers more effectively and provide guests with more interest. For the fact that as the time customer stayed on the interface increases, the possibility that he or she would buy the product increases. Besides, buyers tend to browse details and comments more carefully. This paper proposed a method to predict user decisions based on user behavior. After collection and rebuilding, data were gotten from one product page. This paper uses them for training the Logistic Regression Model. After several iterations, optimal solutions can be obtained using the steepest descent method, and user behavior can be predicted. This paper uses the F1 to evaluate the model by combining the confusion matrix. Our method opens a new route to analyze and predict whether users will achieve specific behavior. It can be extended to more areas to perform more functions, like indicating whether the user is on schedule repayment.

Citation Download Citation

Luyao Shan, Honghao Liu, and Ruoxi Wang "User behavior prediction based on machine learning", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121672K (7 March 2022); https://doi.org/10.1117/12.2628793

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