Research on User Consumption Behavior Prediction of E-commerce Platforms Based on Bayesian Optimization

With the rapid development of computer science, data-driven intelligent decision-making has been widely applied in various fields. In e-commerce, user behavior prediction has become a crucial tool for enhancing user experience and maximizing business profitability. While traditional models have laid the foundation for user consumption behavior prediction, they often struggle to capture the fast-changing and complex nature of today’s consumer preferences, making it more challenging to adapt to the dynamic e-commerce landscape. This paper proposes a novel approach based on Bayesian Optimization (BO) for predicting user consumption behavior on e-commerce platforms. The objective of this paper is to develop an effective prediction model that can accurately forecast users’ purchasing patterns, preferences, and trends by leveraging the strengths of BO. The proposed method incorporates key features, such as the target user’s browsing history and transaction behavior, and improves prediction accuracy by iteratively optimizing the model’s hyperparameters. Experimental results show that the BO-based model significantly outperforms traditional prediction models in terms of accuracy and adaptability to changing user preferences. Additionally, the model demonstrates good scalability, making it applicable to various e-commerce platforms with diverse product offerings. Our findings indicate that incorporating BO into user behavior prediction systems can take personalized marketing to the next level, improving user experience and boosting both conversion rates and overall profitability.