{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T19:07:28Z","timestamp":1768763248130,"version":"3.49.0"},"reference-count":59,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005632","name":"National Centre for Research and Development","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>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\u2019s success hinges on personalized human\u2013computer 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 \u201caccept\u201d, \u201creject\u201d, or \u201cdelay\u201d 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.<\/jats:p>","DOI":"10.3390\/make8010020","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:28:12Z","timestamp":1768555692000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Three-Way Decision Model for E-Commerce Adaptive User Interfaces"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1653-5005","authenticated-orcid":false,"given":"Adam","family":"Wasilewski","sequence":"first","affiliation":[{"name":"Faculty of Management, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7444-2627","authenticated-orcid":false,"given":"Janusz","family":"Sobecki","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"ref_1","unstructured":"Durand-Hayes, S., Gooding, M., Crane, B., Roesch, H., and Pedersen, K. (2023). Decision Points: Sharpening the Pre-Purchase Consumer Experience, PricewaterhouseCoopers (PWC). Technical Report."},{"key":"ref_2","unstructured":"Arora, N., Liu, W.W., Robinson, K., Stein, E., Ensslen, D., Fiedler, L., and Schuler, G. (2021). The Value of Getting Personalization Right\u2014Or Wrong\u2014Is Multiplying, McKinsey. Technical Report."},{"key":"ref_3","unstructured":"Barthel, M., Faraldi, A., Robnett, S., Darp\u00f6, O., Lellouche Tordjman, K., Derow, R., and Ernst, C. (2023). Winning Formulas for E-Commerce Growth, Boston Consulting Group (BCG). Technical Report."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gunawan, R., Anthony, G., and Anggreainy, M.S. (2021, January 12\u201313). The Effect of Design User Interface (UI) E-Commerce on User Experience (UX). Proceedings of the 2021 6th International Conference on New Media Studies (CONMEDIA), Virtually.","DOI":"10.1109\/CONMEDIA53104.2021.9617199"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1080\/0144929X.2015.1132770","article-title":"A model of e-commerce adoption (MOCA): Consumer\u2019s perceptions and behaviours","volume":"35","author":"Grifoni","year":"2016","journal-title":"Behav. Inf. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rahutomo, R., Lie, Y., Perbangsa, A.S., and Pardamean, B. (2020, January 13). Improving Conversion Rates for Fashion e-Commerce with A\/B Testing. Proceedings of the 2020 International Conference on Information Management and Technology (ICIMTech), Virtually.","DOI":"10.1109\/ICIMTech50083.2020.9210947"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wasilewski, A. (2024). Multi-Variant User Interfaces in E-Commerce. A Practical Approach to UI Personalization, Springer.","DOI":"10.1007\/978-3-031-67758-8"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Koukouvis, K., Cubero, R., and Pelliccione, P. (2016). A\/B Testing in E-Commerce Sales Processes, Springer.","DOI":"10.1007\/978-3-319-45892-2_10"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/3530987","article-title":"Toward Practices for Human-Centered Machine Learning","volume":"66","author":"Chancellor","year":"2023","journal-title":"Commun. ACM"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jalil, N. (2021). Introduction to Intelligent User Interfaces (IUIs), IntechOpen.","DOI":"10.5772\/intechopen.97789"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100363","DOI":"10.1016\/j.cosrev.2021.100363","article-title":"Adaptive user interfaces and universal usability through plasticity of user interface design","volume":"40","author":"Miraz","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_12","unstructured":"Browne, D. (2016). Adaptive User Interfaces, Academic Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.ijhcs.2010.01.004","article-title":"Benefits and costs of adaptive user interfaces","volume":"68","author":"Lavie","year":"2010","journal-title":"Int. J. Hum.-Comput. Stud."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1016\/j.procs.2023.10.229","article-title":"Adaptive user interface for workflow-ERP system","volume":"225","author":"Smereka","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","first-page":"663","article-title":"Artificial intelligence-enabled personalization in interactive marketing: A customer journey perspective","volume":"17","author":"Gao","year":"2022","journal-title":"J. Res. Interact. Mark."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"H\u00f6\u00f6k, K. (1997, January 6\u20139). Evaluating the utility and usability of an adaptive hypermedia system. Proceedings of the 2nd International Conference on Intelligent User Interfaces, Orlando, FL, USA.","DOI":"10.1145\/238218.238320"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1002\/mar.21670","article-title":"Personalization in personalized marketing: Trends and ways forward","volume":"39","author":"Chandra","year":"2022","journal-title":"Psychol. Mark."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12193-018-0258-2","article-title":"Model-based adaptive user interface based on context and user experience evaluation","volume":"12","author":"Hussain","year":"2018","journal-title":"J. Multimodal User Interfaces"},{"key":"ref_19","unstructured":"Sutcliffe, A. (2022). Designing for User Engagment: Aesthetic and Attractive User Interfaces, Springer."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bhatia Khan, S., Namasudra, S., Chandna, S., Mashat, A., and Xhafa, F. (2023). Chapter 1\u2014Introduction to human-computer interaction using artificial intelligence. Innovations in Artificial Intelligence and Human-Computer Interaction in the Digital Era, Academic Press. Intelligent Data-Centric Systems.","DOI":"10.1016\/B978-0-323-99891-8.00009-7"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Langley, P. (1997). Machine learning for adaptive user interfaces. Proceedings of the Annual Conference on Artificial Intelligence, Freiburg, Germany, 9\u201312 September 1997, Springer.","DOI":"10.1007\/3540634932_3"},{"key":"ref_22","unstructured":"Monarch, R.M. (2021). Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI, Simon and Schuster."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, N., Zhang, M., Li, J., Kang, E., and Tei, K. (2023, January 15\u201316). Preference Adaptation: User satisfaction is all you need!. Proceedings of the 2023 IEEE\/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), Melbourne, Australia.","DOI":"10.1109\/SEAMS59076.2023.00027"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yigitbas, E., Karakaya, K., Jovanovikj, I., and Engels, G. (2021, January 23\u201324). Enhancing Human-in-the-Loop Adaptive Systems through Digital Twins and VR Interfaces. Proceedings of the 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), SEAMS \u201921, Madrid, Spain.","DOI":"10.1109\/SEAMS51251.2021.00015"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Capel, T., and Brereton, M. (2023). What is Human-Centered about Human-Centered AI? A Map of the Research Landscape. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI \u201923, Hamburg, Germany, 23\u201328 April 2023, Association for Computing Machinery.","DOI":"10.1145\/3544548.3580959"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1080\/10447318.2020.1741118","article-title":"Human-centered artificial intelligence: Reliable, safe & trustworthy","volume":"36","author":"Shneiderman","year":"2020","journal-title":"Int. J. Hum.-Comput. Interact."},{"key":"ref_27","first-page":"326","article-title":"Personalization research in e-commerce\u2014A state of the art review (2000\u20132008)","volume":"11","author":"Adolphs","year":"2010","journal-title":"J. Electron. Commer. Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rathnayake, N., Meedeniya, D., Perera, I., and Welivita, A. (2019, January 3\u20135). A Framework for Adaptive User Interface Generation based on User Behavioural Patterns. Proceedings of the 2019 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka.","DOI":"10.1109\/MERCon.2019.8818825"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Johnston, V., Black, M., Wallace, J., Mulvenna, M., and Bond, R. (2019, January 10\u201313). A framework for the development of a dynamic adaptive intelligent user interface to enhance the user experience. Proceedings of the 31st European Conference on Cognitive Ergonomics, Belfast, UK.","DOI":"10.1145\/3335082.3335125"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/978-981-96-6515-0_23","article-title":"Utilizing Machine Learning Methods for Customer Segmentation Analysis","volume":"1417","author":"Singh","year":"2025","journal-title":"Lect. Note. Netw. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102735","DOI":"10.1016\/j.jvcir.2019.102735","article-title":"E-commerce personalized recommendation analysis by deeply-learned clustering","volume":"71","author":"Wang","year":"2020","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gheibi, O., and Weyns, D. (2022). Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning. Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS \u201922, Pittsburgh, PA, USA, 18\u201323 May 2022, Association for Computing Machinery.","DOI":"10.1145\/3524844.3528052"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wasilewski, A., and Wasilewska, B. (2025). Data-Driven E-Commerce UI Personalization: Going Beyond Product Recommendations. Int. J. Hum.\u2013Comput. Interact., 1\u201324.","DOI":"10.1080\/10447318.2025.2558014"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s10257-023-00640-4","article-title":"A review on customer segmentation methods for personalized customer targeting in e-commerce use cases","volume":"21","author":"Meisen","year":"2023","journal-title":"Inf. Syst. e-Bus. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1109\/TCC.2020.3015769","article-title":"Resource usage cost optimization in cloud computing using machine learning","volume":"10","author":"Osypanka","year":"2020","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1080\/00913367.2021.1887013","article-title":"When E-Commerce Personalization Systems Show and Tell: Investigating the Relative Persuasive Appeal of Content-Based versus Collaborative Filtering","volume":"51","author":"Liao","year":"2022","journal-title":"J. Advert."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s41060-022-00343-y","article-title":"Semantic enhanced Markov model for sequential E-commerce product recommendation","volume":"15","author":"Nasir","year":"2023","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"15","DOI":"10.18267\/j.aip.167","article-title":"An image-based product recommendation for E-commerce applications using convolutional neural networks","volume":"11","author":"Alamdari","year":"2022","journal-title":"Acta Inform. Pragensia"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"162","DOI":"10.35870\/ijsecs.v3i2.1527","article-title":"E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering","volume":"3","author":"Wattimena","year":"2023","journal-title":"Int. J. Softw. Eng. Comput. Sci. (IJSECS)"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cao, J. (2023). E-Commerce Big Data Mining and Analytics, Springer Nature.","DOI":"10.1007\/978-981-99-3588-8"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1080\/0144929021000009054","article-title":"Critical design factors for successful e-commerce systems","volume":"21","author":"Kim","year":"2002","journal-title":"Behav. Inf. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Deuschel, T., and Scully, T. (2016, January 12\u201316). On the Importance of Spatial Perception for the Design of Adaptive User Interfaces. Proceedings of the 2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), Augsburg, Germany.","DOI":"10.1109\/SASO.2016.13"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1177\/2278533719887451","article-title":"Underlying factors influencing consumers\u2019 trust and loyalty in E-commerce","volume":"8","author":"Aslam","year":"2020","journal-title":"Bus. Perspect. Res."},{"key":"ref_44","unstructured":"Nah, F.F.H., and Siau, K. (2019). The Role of User Emotions for Content Personalization in e-Commerce: Literature Review. Proceedings of the HCI in Business, Government and Organizations, eCommerce and Consumer Behavior, Orlando, FL, USA, 26\u201331 July 2019, Springer."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3834","DOI":"10.1080\/0144929X.2023.2286535","article-title":"Data-driven digital nudging: A systematic literature review and future agenda","volume":"43","author":"Sadeghian","year":"2023","journal-title":"Behav. Inf. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Leach, P.J., Salz, R., and Mealling, M.H. (2005). A Universally Unique IDentifier (UUID) URN Namespace, DataPower Technology, Inc.. RFC 4122.","DOI":"10.17487\/rfc4122"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A Dendrite Method for Cluster Analysis","volume":"3","author":"Calinski","year":"1974","journal-title":"Commun. Stat.-Theory Methods"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/01969727308546046","article-title":"A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters","volume":"3","author":"Dunn","year":"1973","journal-title":"J. Cybern."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A Cluster Separation Measure","volume":"PAMI-1","author":"Davies","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13638-021-01910-w","article-title":"A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm","volume":"2021","author":"Shi","year":"2021","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_52","unstructured":"Dopson, E. (2025, November 15). Ecommerce Customer Retention Marketing: How to Use Emails, Loyalty Programs & Communities to Improve Retention. Available online: https:\/\/www.shopify.com\/blog\/customer-retention-program."},{"key":"ref_53","unstructured":"Saleh, K. (2023). The Average Website Conversion Rate by Industry (Updated 2023), Invesp. Technical Report."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"113559","DOI":"10.1016\/j.asoc.2025.113559","article-title":"Multi-criteria selection of data clustering methods for e-commerce personalization","volume":"182","author":"Przyborowski","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_55","first-page":"2579","article-title":"Viualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Kohavi, R., Tang, D., and Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A\/B Testing, Cambridge University Press.","DOI":"10.1017\/9781108653985"},{"key":"ref_57","unstructured":"(2026, January 05). How to Wrap Your Head Around Minimum Detectable Effect (MDE). Available online: https:\/\/www.convert.com\/blog\/a-b-testing\/minimum-detectable-effect-mde-ab-testing\/."},{"key":"ref_58","unstructured":"(2026, January 05). Understanding Minimum Detectable Effect (MDE). Available online: https:\/\/help.vwo.com\/hc\/en-us\/articles\/36876638315929-Understanding-Minimum-Detectable-Effect-MDE."},{"key":"ref_59","unstructured":"(2026, January 05). MDE in A\/B Testing: Setting Realistic Expectations for Your Experiments. Available online: https:\/\/www.statsig.com\/perspectives\/mde-ab-testing-expectations."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/8\/1\/20\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T05:16:35Z","timestamp":1768713395000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/8\/1\/20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,16]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["make8010020"],"URL":"https:\/\/doi.org\/10.3390\/make8010020","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,16]]}}}