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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]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Data-Driven E-Commerce UI Personalization: Going Beyond Product Recommendations.
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  Data: <searchLink fieldCode="AR" term="%22Wasilewski%2C+Adam%22">Wasilewski, Adam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> adam.wasilewski@pwr.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Wasilewska%2C+Barbara%22">Wasilewska, Barbara</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. May2026, Vol. 42 Issue 10, p7185-7208. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Web+personalization%22">Web personalization</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22User+experience%22">User experience</searchLink><br /><searchLink fieldCode="DE" term="%22User+interfaces%22">User interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Market+segmentation%22">Market segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+commerce%22">Electronic commerce</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/10447318.2025.2558014
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 7185
    Subjects:
      – SubjectFull: Web personalization
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: User experience
        Type: general
      – SubjectFull: User interfaces
        Type: general
      – SubjectFull: Market segmentation
        Type: general
      – SubjectFull: Electronic commerce
        Type: general
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      – TitleFull: Data-Driven E-Commerce UI Personalization: Going Beyond Product Recommendations.
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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