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.

The research is situated within the debate on fair, sustainable, and inclusive information systems. It focuses on the analysis of the digital accessibility of Amazon’s marketplaces and widely accepted standards, alongside legal differences related to the European Accessibility Act (EAA). The study aims to analyze the intersection of digital accessibility and fairness on global e-commerce platforms. In the context of sustainable and inclusive software systems, the study critically assesses the extent to which Amazon adheres to the Web Content Accessibility Guidelines (WCAG) in various regional domains. The purpose of the article is to present the proposed approach to studying digital accessibility for more sustainable and fair e-commerce systems. For the purpose of the study, a measure of digital accessibility was proposed, and a quantitative analysis of the level of WCAG compliance was conducted based on the results obtained after analyzing the homepages and selected product pages. The empirical research was repeated twice, six months apart, to observe the direction of change in the context of the need for some marketplaces to comply with EAA regulations. The analysis reveals significant disparities in accessibility implementation, often correlating with the presence or absence of international regulatory mandates, such as the EAA. These findings highlight a systemic trade-off in which compliance-driven enhancements in certain locations coincide with accessibility deficits elsewhere. The study contributes to the discourse on fairness in software systems by demonstrating how uneven regulatory pressures can result in inequitable user experiences. Using the proposed aggregate measure, an analysis of changes in the digital accessibility of all Amazon marketplaces over time was conducted, showing that regulations affect the fairness of information systems. In addition, the effectiveness of the introduced metric was verified, opening up possibilities for its wider use to improve the quality of information systems. • Digital accessibility is a critical dimension of fairness and inclusivity. • Compliance with accessibility standards varies across regional versions. • International regulations play a pivotal role in enforcing fairness. • Design choices that aid some markets may unintentionally marginalize others.

E-commerce is a very popular method that lets consumers to purchase goods and services.The ability to purchase items online has increased the need for effective recommendation systems.Such recommendations usually relete to products in which the customer may be interested.However, there are wider opportunities to tailor e-commerce to individual customer needs and behaviour.In this paper, the architecture of the e-commerce platform (named AIM 2 ), which allows providing a dedicated interface to selected user groups, is discussed.A key component of the platform is the module responsible for dividing customers into groups, using selected clustering methods.Each of the implemented methods can be parameterised to adapt the customer segmentation to a given e-commerce business owner's requirements.This article describes the results of an analysis of the impact of selected methods and parameters on clustering results.Moreover, it identifies key metrics that should be considered when selecting clustering conditions during the implementation of the platform.Finally, the main results of the pilot implementation of AIM 2 are presented to assess the effectiveness of the multi-variant user interface.

In this paper, the problem of user interface recommendations for workflow management systems is investigated. The user interface is automatically adapted using a software tool based on content-based filtering. This tool collects information about the way processes are carried out in an organization, analyzes and processes the data, and recommends the next action to the user in order to increase efficiency, facilitate training, and improve decision-making. The proposed tool was verified in the real environment within three organizations. For each organization, after at least several weeks of learning, the tool was able to offer suggestions that were selected by real users.

The main goal of the presented paper is to propose a method for adaptation of the user interface of workflow software to increase its efficiency, reduce the number of errors, and improve its UX. The authors assumed that the system adaptation will be achieved by application to intelligent methods for modeling user as well as system. In order to do this, a special tool for data gathering has been designed and in the next steps of the research, this tool will be also implemented in a real environment. A unique value of the paper is that after many years of theoretical research, the first attempt to implement a practical solution for self-adapting and the personalized interface for workflow systems was done.