Abstract
As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce consumers. Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics. And then use our proposed model to make predictions. Experiments show that the model’s overall performance with increased interactive behavior features is better and has higher accuracy. Compared with the existing prediction models, the improved deep forest model has certain advantages, which not only improves the prediction accuracy but also reduces the cost of training time.
Highlights
With the advent of the big data era, available information grows explosively
Previous studies on consumer behavior focused on analyzing influencing factors of repurchase behavior [9,10,11,12, 15], but this paper focuses on predicting consumer repurchase behavior
According to the combination of the real category of the sample and the predicted category of the model in the paper, the results can be divided into four types: true positive (TP), false-positive (FP), true negative (TN), and false-negative (FN)
Summary
With the advent of the big data era, available information grows explosively. By 2020, the global information volume has reached 35 trillion gigabytes [1]. Most of the data come from historical records left by users on the Internet, such as web search [2], clicks [3], etc. By June 2020, the number of online shopping users in China has reached 749 million [4]. Compared with 2018, the number increased by nearly 139 million in 2020, accounting for 79.7 percent of the Internet users. The scale of mobile online shopping has reached 747 million. Compared with 2018, 2020 increased by 156 million, accounting for 80.1 percent of mobile Internet users. By mining big data’s hidden laws, meaningless data can realize value [6, 7]. In e-commerce, enterprises can predict users’ needs and preferences based on the consumers’ behavioral data [8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
