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Data-Driven E-Commerce UI Personalization: Going Beyond Product Recommendations.

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Title: Data-Driven E-Commerce UI Personalization: Going Beyond Product Recommendations.
Authors: Wasilewski, Adam1 (AUTHOR) adam.wasilewski@pwr.edu.pl, Wasilewska, Barbara2 (AUTHOR)
Source: International Journal of Human-Computer Interaction. May2026, Vol. 42 Issue 10, p7185-7208. 24p.
Subjects: Web personalization, Artificial intelligence, Machine learning, User experience, User interfaces, Market segmentation, Electronic commerce
Abstract: User experience is a critical driver of customer loyalty and business efficiency in e-commerce. Personalization, tailored to individual user needs, is a primary method for enhancing this experience. While product recommendations are common, this paper explores the broader potential of personalization driven by artificial intelligence and machine learning. We propose a comprehensive model that utilizes customer behavioral data for applications beyond recommendations, including dynamic customer segmentation, the delivery of multivariant user interfaces, automated content generation, and even the promotion of socially desirable behaviors. The central premise is that a single, static interface is insufficient for diverse user groups. To validate this concept, we conducted an experimental study. The results support our model's validity, confirming the benefits of this adaptive approach and demonstrating how data-driven personalization can create more effective e-commerce environments while informing future research and applications directions. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:User experience is a critical driver of customer loyalty and business efficiency in e-commerce. Personalization, tailored to individual user needs, is a primary method for enhancing this experience. While product recommendations are common, this paper explores the broader potential of personalization driven by artificial intelligence and machine learning. We propose a comprehensive model that utilizes customer behavioral data for applications beyond recommendations, including dynamic customer segmentation, the delivery of multivariant user interfaces, automated content generation, and even the promotion of socially desirable behaviors. The central premise is that a single, static interface is insufficient for diverse user groups. To validate this concept, we conducted an experimental study. The results support our model's validity, confirming the benefits of this adaptive approach and demonstrating how data-driven personalization can create more effective e-commerce environments while informing future research and applications directions. [ABSTRACT FROM AUTHOR]
ISSN:10447318
DOI:10.1080/10447318.2025.2558014