Online Purchase Behavior Prediction Model Based on Recurrent Neural Network and Naive Bayes

• Published in: Journal of theoretical and applied electronic commerce research 2024-12, Vol.19 (4), p.3461-3476
• Main Authors: Zhang, Chaohui, Liu, Jiyuan, Zhang, Shichen
• Format: Article
• Language: English
• Subjects: Algorithms; Bayesian analysis; behavior sequence; Big Data; Collaboration; Commodities; Decomposition; Electronic commerce; Markov analysis; Neural networks; online purchase behavior; prediction model; Prediction models; Preferences; Recurrent neural networks; Time series; User behavior; User needs
• ISSN: 0718-1876; 0718-1876
• DOI: 10.3390/jtaer19040168

In the current competition process of e-commerce platforms, the technical and algorithmic wars that can quickly grasp user needs and accurately recommend target commodities are the core tools of platform competition. At the same time, the existing online purchase behavior prediction models lack consideration of time series features. This paper combines the Recurrent Neural Network, which is more suitable for the commodity recommendation scenario of the e-commerce platform, with Naive Bayes, which is simple in logic and efficient in operation and constructs the online purchase behavior prediction model RNN-NB, which can consider the features of time series. The RNN-NB model is trained and tested using 3 million time series data with purchase behavior provided by the Ali Tianchi big data platform. The prediction effect of the RNN-NB model and Naive Bayes model is evaluated and compared respectively under the same experimental conditions. The results show that the overall prediction effect of the RNN-NB model is better and more stable. In addition, through the analysis of user time series features, it can be found that the possibility of user purchase is negatively correlated with the length of time series, and merchants should pay more attention to those users with shorter time series in commodity recommendation and targeted offers. The contributions of this paper are as follows: (1) By constructing an online purchasing behavior model RNN-NB, which integrates the N vs 1 structure Recurrent Neural Network and naive Bayesian model, the validity limitations of some single-architecture recommendation algorithms are solved. (2) Based on the existing naive Bayesian model, the prediction accuracy of online purchasing behavior is further improved. (3) The analysis based on the features of the time series provides new ideas for the research of later scholars and new guidance for the marketing of platform merchants.