Applications of Machine Learning Algorithms in Predicting User’s Purchasing Behavior

Abstract

With the rapid development of big data in the Internet era, accurately identifying consumers’ purchase intention and predicting their future purchase behavior among the massive user behaviors are crucial for business decisions. The purpose of this paper is to analyze the advantages and disadvantages of multiple supervised learning algorithms and integrated learning algorithms, as well as their applications and performances in predicting users’ purchasing behaviors. The paper concludes that some traditional algorithms have been consistently used due to their simplicity and interpretability, while the more cutting-edge algorithms have a greater advantage in characterizing specific aspects around an innovative core idea. Different algorithms have their own highlights and limitations in prediction, and researchers can choose them according to the dataset and prediction needs. At the same time, this paper emphasizes that combining models that complement each other’s strengths will maximize efficiency and accuracy when using fusion methods. This paper compiles and compares practical machine learning algorithms today, and analyzes the future direction of predictive modeling and areas worthy of further exploration, such as language and image processing, which can provide a reference for enterprises with the need of user behavior prediction in the development of marketing plans.