Enhanced E-Commerce Personalization Through AI-Powered Content Generation Tools

This paper proposes a new framework for personalizing e-commerce, which integrates multivariant user interfaces (MultiUI) with AI-generated content (AIGC). By utilizing customer behavioral data, our approach customizes both the visual layout and product descriptions for specific customer segments. This addresses the current research gap that often overlooks the synergy between UI design and content personalization. We conducted an empirical study to demonstrate the effectiveness of this integrated approach, showing that personalized user interface variants significantly improve customer engagement and conversion rates. In addition, we explore the potential of AIGC by using behavioral clusters to generate customized product descriptions. This showcases how AI can improve the relevance and appeal of product information, contributing to a more engaging and effective e-commerce experience. Although our initial findings using a simplified approach with ChatGPT are promising, future research will focus on refining AIGC models by incorporating domain-specific knowledge and leveraging comprehensive customer behavior data to generate highly tailored product descriptions. This research advances information processing in e-commerce by demonstrating how AI can be used to extract valuable insights from customer data, adapt UI designs, and generate personalized content, ultimately leading to more profitable online shopping experiences. Experimental studies showed that only about 10% of the most popular words were repeated in the product descriptions generated for the three different clusters. At the same time, two-thirds of the most popular words were dominant in only one of the clusters, confirming the satisfactory degree of matching descriptions to the specifics of customer groups.