Machine Learning-Based Three-Way Decision Model for E-Commerce Adaptive User Interfaces

Abstract

In the world of e-commerce, ensuring customer satisfaction and retention depends on delivering an optimal user experience. As the primary point of contact between businesses and consumers, a user interface’s success hinges on personalized human–computer interaction. The goal of this paper is to introduce the concept of a self-adaptive multi-variant user interface based on a novel application of a three-way decision-making model, which allows for “accept”, “reject”, or “delay” decisions on UI changes. The proposed framework enables the delivery of a multi-variant e-commerce user interface. It leverages human-centered machine learning to identify homogeneous groups of customers for whom a layout tailored to their behavior can be offered. The functionality of the solution was verified through pilot implementation and experimental studies. The results positively validated the three-way decision algorithm and highlighted clear directions for its refinement. The primary contribution of this work is the novel adaptation of the three-way decision model to create an automated framework for e-commerce UI personalization, moving beyond the limitations of traditional binary A/B testing. This study demonstrates the practical feasibility of using a self-adaptive, multi-variant interface to significantly improve user experience and key business metrics. These results confirm the feasibility and effectiveness of using self-adaptive e-commerce interfaces to improve the user experience. The proposed framework represents a promising solution to the challenges posed by static interfaces and demonstrates the potential for wider application in the e-commerce domain and beyond.