Adaptive user interface for workflow-ERP system

Smereka, Marcin; Kołaczek, Grzegorz; Sobecki, Janusz; Wasilewski, Adam

doi:10.1016/j.procs.2023.10.229

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Today, various approaches are commonly used for personalization in adaptive web shop interfaces, including AI-based methods such as collaborative filtering (CF) [15], CF based on deep learning [16], and its modification that uses the relationships between items rather than users (item-based CF-IBCF) as the basis for inference [17], case-based reasoning (CBR) [18], the RFMT (recency, frequency, monetary, time) model [19], data mining [20], and clustering [21]. UI personalization is applicable across diverse IT systems, whether within organizations (e.g., enterprise resource planning-ERP [22]), supporting interorganizational collaboration (e.g., workflow [23]), or dedicated to customers (e.g., e-commerce [24]).…”

Section: Personalization Of the User Interfacementioning

confidence: 99%

Functional Framework for Multivariant E-Commerce User Interfaces

Wasilewski

2024

JTAER

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Modern e-businesses heavily rely on advanced data analytics for product recommendations. However, there are still untapped opportunities to enhance user interfaces. Currently, online stores offer a single-page version to all customers, overlooking individual characteristics. This paper aims to identify the essential components and present a framework for enabling multiple e-commerce user interfaces. It also seeks to address challenges associated with personalized e-commerce user interfaces. The methodology includes detailing the framework for serving diverse e-commerce user interfaces and presenting pilot implementation results. Key components, particularly the role of algorithms in personalizing the user experience, are outlined. The results demonstrate promising outcomes for the implementation of the pilot solution, which caters to various e-commerce user interfaces. User characteristics support multivariant websites, with algorithms facilitating continuous learning. Newly proposed metrics effectively measure changes in user behavior resulting from different interface deployments. This paper underscores the central role of personalized e-commerce user interfaces in optimizing online store efficiency. The framework, supported by machine learning algorithms, showcases the feasibility and benefits of different page versions. The identified components, challenges, and proposed metrics contribute to a comprehensive solution and set the stage for further development of personalized e-commerce interfaces.

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Section: Personalization Of the User Interfacementioning

confidence: 99%

Functional Framework for Multivariant E-Commerce User Interfaces

Wasilewski

2024

JTAER

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Introduction to the Personalization in E-commerce

Wasilewski

2024

Progress in IS

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One Size Does Not Fit All: Multivariant User Interface Personalization in E-Commerce

Wasilewski,

Kolaczek

2024

IEEE Access

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One of the most visible manifestations of the changes brought about by the digitization of everyday life is undoubtedly the spread of electronic commerce. It is difficult to think of the digital economy without considering transactions through electronic channels. In turn, the user interface (UI) is the key to ecommerce, as it is usually the first and primary point of contact between business and consumer. A key trend in e-commerce is the personalization of communications, which can improve the user experience, increase customer satisfaction and deliver tangible business benefits. Today, it is technically possible to base this personalization on an analysis of user behavior using artificial intelligence and machine learning techniques. A common form of personalization in e-commerce is the use of product recommendation systems, but the user interface can be tailored much more extensively. The approach described and discussed in this paper is a multivariant user interface that allows the layout to be tailored to the characteristics, attributes, and behaviors of customer groups generated using machine learning techniques. The results of the research carried out make it possible to verify the practicality of the proposed solution and provide an opportunity to identify development directions that take into account the potential of artificial intelligence. The application of the concept described in the paper is broad, covering all aspects of e-commerce design that require compromises when serving a single UI variant, but allow flexibility and customization for different users when serving a multivariant UI.

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Adaptive user interface for workflow-ERP system

Cited by 3 publications

References 9 publications

Functional Framework for Multivariant E-Commerce User Interfaces

Functional Framework for Multivariant E-Commerce User Interfaces

Introduction to the Personalization in E-commerce

One Size Does Not Fit All: Multivariant User Interface Personalization in E-Commerce

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