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Clustering Methods for Adaptive e-Commerce User Interfaces

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Rough Sets (IJCRS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14481))

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Abstract

Typical online shops have one interface provided to all users, regardless of their use of the shop. Meanwhile, user behavior varies and therefore different interfaces could be provided to different user groups. Various methods can be used to cluster users, including those using artificial intelligence (AI) methods. AI-based personalization allows e-commerce businesses to provide tailored recommendations to each individual customer based on preferences, purchase history, and behavior on the website. This article presents a study of the impact of an AI-based clustering method on the effectiveness of a dedicated user interface implemented and delivered to the customers of an e-shop. The first study included five methods, and two of them - agglomerative clustering and K-means clustering - were selected for detailed analysis. For both of these methods, an in-depth research was carried out and the impact of the clustering method on the quality of user clusters, as measured by the effectiveness of the dedicated interface in relation to the effectiveness of the default interface, was verified.

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Acknowledgements

Project co-funded by the National Centre for Research and Development under the Sub-Action 1.1.1 of the Operational Programme Intelligent Development 2014-2020.

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Authors and Affiliations

  1. Fast White Cat S.A., Wrocław, Poland

    Adam Wasilewski

  2. Faculty of Management, Wrocław University of Science and Technology, Wrocław, Poland

    Adam Wasilewski

  3. QED Software, Warsaw, Poland

    Mateusz Przyborowski

  4. Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland

    Mateusz Przyborowski

Authors
  1. Adam Wasilewski
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  2. Mateusz Przyborowski
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Corresponding author

Correspondence to Adam Wasilewski .

Editor information

Editors and Affiliations

  1. IRCCS Istituto Ortopedico Galeazzi, Milano, Italy

    Andrea Campagner

  2. Ghent University, Ghent, Belgium

    Oliver Urs Lenz

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Shuyin Xia

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. AGH University of Science and Technology, Kraków, Poland

    Jarosław Wąs

  6. University of Regina, Regina, SK, Canada

    JingTao Yao

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The datasets used during the current study are available from authors on reasonable request.

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Wasilewski, A., Przyborowski, M. (2023). Clustering Methods for Adaptive e-Commerce User Interfaces. In: Campagner, A., Urs Lenz, O., Xia, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_35

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  • DOI: https://doi.org/10.1007/978-3-031-50959-9_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50958-2

  • Online ISBN: 978-3-031-50959-9

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