Enhancing Community E-Commerce Repurchase Prediction Through Information Entropy Analysis

Abstract

In this study, we delve into the prediction and recommendation algorithm for user repurchase behavior within the community e-commerce landscape, leveraging the framework of information entropy. By analyzing consumer behavior data from a product-centered perspective, we observe that the majority of consumers engage in approximately 0-8 information search clicks before making a purchase, primarily focusing on product details, with an average of about 4 browsing interactions. Subsequently, a minimal portion of consumers exhibit 0-5 instances of preference behavior after browsing activities. Similarly, consumers who add items to their shopping carts typically engage in this behavior from 0 to 4 times, showcasing limited participation. Leveraging attribute probability, information entropy serves as a driving force for predicting community e-commerce users' repurchase behavior, contrasting with traditional outlier detection methods and underscoring the advantages of information entropy.