Personalization of Adaptive Interfaces Based on User Data Analysis: A Systematic Review
Abstract
Introduction: Adaptive interfaces that personalize based on user data analysis have gained prominence as essential solutions for improving user experience in digital systems. Objective: This study aims to conduct a systematic review to identify personalization techniques, challenges, and trends in adaptive interface development. Methodology or Steps: A systematic review was conducted analyzing 46 studies published between 2018-2024, following established protocols for literature review. Results: The analysis revealed predominant use of artificial intelligence techniques, emphasis on behavioral data collection and explicit preferences, with challenges including model complexity and need for high-quality data. Despite obstacles, there is growing scientific interest driven by potential to enhance user experience and accommodate diverse profiles.
Keywords:
Adaptive Interfaces, Personalization, User Data, Artifcial Intelligence, Usability
References
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Cherukuri, B. R. (2024). Development of design patterns with adaptive user interface for cloud native microservice architecture using deep learning with iot. In 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), pages 1866–1871. IEEE.
Chew, J. Y., Kawamoto, M., Okuma, T., Yoshida, E., and Kato, N. (2021). Multi-modal approach to evaluate adaptive visual stimuli of remote operation system using gaze behavior. In Proceedings of International Journal of Industrial Ergonomics, page 103223. Elsevier.
Conati, C., Barral, O., Putnam, V., and Rieger, L. (2021). Toward personalized xai: A case study in intelligent tutoring systems. In Proceedings of Artificial Intelligence, page 103503. Elsevier.
Costa, A., Silva, F., and Moreira, J. J. (2024). Towards an ai-driven user interface design for web applications. In Proceedings of Procedia Computer Science, pages 179–186. Elsevier.
Diego-Mas, J. A., Garzon-Leal, D., Poveda-Bautista, R., and Alcaide-Marzal, J. (2019). User-interfaces layout optimization using eye-tracking, mouse movements and genetic algorithms. In Proceedings of Applied Ergonomics, pages 197–209. Elsevier.
ElSayed, N., Veas, E., and Schmalstieg, D. (2024). Agents of mask: Mobile analytics from situated knowledge. In Interactions, pages 48–55, New York, NY, USA. Association for Computing Machinery.
Finne, R., Larsson, L., Mylonopoulou, V., Andreasson, S., Hjelm, T., Rost, M., Weilenmann, A., and Torgersson, O. (2022). Reversed multi-layer design as an approach to designing for digital seniors. In Nordic Human-Computer Interaction Conference, page 5, New York, NY, USA. Association for Computing Machinery.
Gomi, R., Takashima, K., Onishi, Y., Fujita, K., and Kitamura, Y. (2023). Ubisurface: A robotic touch surface for supporting mid-air planar interactions in room-scale vr. In Proceedings of ACM Human-Computer Interaction, page 443, New York, NY, USA. Association for Computing Machinery.
Grua, E. M., De Sanctis, M., Malavolta, I., Hoogendoorn, M., and Lago, P. (2022). An evaluation of the effectiveness of personalization and self-adaptation for e-health apps. In Proceedings of Information and Software Technology, page 106841. Elsevier.
Iglesias, A., Yue, T., Arellano, C., Ali, S., and Sagardui, G. (2018). Model-based personalized visualization system for monitoring evolving industrial cyber-physical system. In 2018 25th Asia-Pacific Software Engineering Conference (APSEC), pages 532–541. IEEE.
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Jo, S.-M. and Cho, S.-B. (2019). A personalized context-aware soft keyboard adapted by random forest trained with additional data of same cluster. In Proceedings of Neurocomputing, pages 17–27. Elsevier.
Khamaj, A. and Ali, A. M. (2024). Adapting user experience with reinforcement learning: Personalizing interfaces based on user behavior analysis in real-time. In Proceedings of Alexandria Engineering Journal, pages 164–173. Elsevier.
Khan, M. and Khusro, S. (2023). Towards the design of personalized adaptive user interfaces for smart tv viewers. In Proceedings of Journal of King Saud University - Computer and Information Sciences, page 101777. Elsevier.
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., and Linkman, S. (2009). Systematic literature reviews in software engineering – a systematic literature review. Information and Software Technology, 51(1):7–15.
Kolekar, S. V., Pai, R. M., and Pai M.M., M. (2018). Adaptive user interface for moodle based e-learning system using learning styles. In Proceedings of Procedia Computer Science, pages 606–615. Elsevier.
Laguna Salvadó, L., Villeneuve, E., Masson, D., Abi Akle, A., and Bur, N. (2022). Decision support system for technology selection based on multi-criteria ranking: Application to nzeb refurbishment. In Proceedings of Building and Environment, page 108786. Elsevier.
Lu, F., Chen, M., Hsu, H., Deshpande, P., Wang, C. Y., and MacIntyre, B. (2024). Adaptive content placement in mixed reality through empirical user behavioral patterns. In 2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pages 199–204. IEEE.
Madduri, M. M., Burden, S. A., and Orsborn, A. L. (2023). Biosignal-based co-adaptive user-machine interfaces for motor control. In Proceedings of Current Opinion in Biomedical Engineering, page 100462. Elsevier.
Manoharan, S. K., Megalingam, R. K., and Lazar, A. M. A. (2024). Reconfgurable ui/ux design for robotic applications based on user experience. In 2024 4th Asian Conference on Innovation in Technology (ASIANCON), pages 1–6. IEEE.
Melchiorre, A. B., Penz, D., Ganhör, C., Lesota, O., Fragoso, V., Friztl, F., Parada-Cabaleiro, E., Schubert, F., and Schedl, M. (2022). Emomtb: Emotion-aware music tower blocks. In Proceedings of the 2022 International Conference on Multimedia Retrieval, pages 206–210, New York, NY, USA. Association for Computing Machinery.
Miraz, M. H., Ali, M., Excell, P. S., and Khan, S. (2021). Ai-based culture independent pervasive m-learning prototype using ui plasticity design. In Proceedings of Computers, Materials and Continua, pages 1021–1039. Tech Science Press.
Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. (2010). Preferred reporting items for systematic reviews and meta-analyses: The prisma statement. In Proceedings of the International Journal of Surgery, pages 336–341. Elsevier.
Märtin, C. and Herdin, C. (2024). Enabling real-time adaptations for individualized customer experience in user-centered e-business applications. In Proceedings of Procedia Computer Science, pages 1425–1432. Elsevier.
Okopnyi, P., Nordberg, O. E., and Guribye, F. (2024). Against generative ui. In Proceedings of the Halfway to the Future Symposium, page 12, New York, NY, USA. Association for Computing Machinery.
Pereira, R., Darin, T., and Silveira, M. S. (2024). Grandihc-br: Grand research challenges in human-computer interaction in brazil for 2025-2035. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, IHC ’24, New York, NY, USA. Association for Computing Machinery.
Pfeuffer, K. and Li, Y. (2018). Analysis and modeling of grid performance on touchscreen mobile devices. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pages 1–12, New York, NY, USA. Association for Computing Machinery.
Rathnayake, N., Meedeniya, D., Perera, I., and Welivita, A. (2019). A framework for adaptive user interface generation based on user behavioural patterns. In 2019 Moratuwa Engineering Research Conference (MERCon), pages 698–703. IEEE.
Rei, D., Clavel, C., Martin, J.-C., and Ravenet, B. (2024). Adapting goals and motivational messages on smartphones for motivation to walk. In Proceedings of Smart Health, page 100482. Elsevier.
Romero, O. J., Haig, A., Kirabo, L., Yang, Q., Zimmerman, J., Tomasic, A., and Steinfeld, A. (2020). A long-term evaluation of adaptive interface design for mobile transit information. In 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services, page 39, New York, NY, USA. Association for Computing Machinery.
Silva, K. G. G. H., Abeyasekare, W. A. P. S., Dasanayake, D. M. H. E., Nandisena, T. B., Kasthurirathna, D., and Kugathasan, A. (2021). Dynamic user interface personalization based on deep reinforcement learning. In 2021 3rd International Conference on Advancements in Computing (ICAC), pages 25–30. IEEE.
Smereka, M., Kołaczek, G., Sobecki, J., and Wasilewski, A. (2023). Adaptive user interface for workfow-erp system. In Proceedings of Procedia Computer Science, pages 2381–2391. Elsevier.
Sobecki, J., Wasilewski, A., and Kołaczek, G. (2020). Self-adaptation of workfow business software to the user’s requirements and behavior. In Proceedings of Procedia Computer Science, pages 3506–3513. Elsevier.
Sun, Q., Xue, Y., and Song, Z. (2024). Adaptive user interface generation through reinforcement learning: A data-driven approach to personalization and optimization. In 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC), pages 1386–1391. IEEE.
Suryani, M., Sensuse, D., and Santoso, H. (2024). An initial user model design for adaptive interface development in learning management system based on cognitive load. In Proceedings of Cognition, Technology Work, pages 653–672. Springer.
Wang, J. and Li, J. (2024). Human body features recognition based adaptive user interface for extra-large touch screens. In Proceedings of Displays, page 102838. Elsevier.
Wang, W., Gai, W., Zang, C., Bao, X., and Yang, C. (2023). Personalized recommendation of user interfaces based on foot interaction. In 2023 IEEE Smart World Congress (SWC), pages 1–8. IEEE.
Wang, W., Khalajzadeh, H., and Grundy, J. (2024a). Adaptive user interfaces in systems targeting chronic disease: A systematic literature review. In Proceedings of User Modeling and User-Adapted Interaction, pages 853–920. Springer.
Wang, W., Khalajzadeh, H., Grundy, J., Madugalla, A., and Obie, H. O. (2024b). Adaptive user interfaces for software supporting chronic disease. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Society, pages 118–129, New York, NY, USA. Association for Computing Machinery.
Wu, J., Todi, K., Chan, J., Myers, B. A., and Lafreniere, B. (2024). Framekit: A tool for authoring adaptive uis using keyframes. In Proceedings of the 29th International Conference on Intelligent User Interfaces, pages 660–674, New York, NY, USA. Association for Computing Machinery.
Yeo, H.-S., Wu, E., Kim, D., Lee, J., il Kim, H., Oh, S. Y., Takagi, L., Woo, W., Koike, H., and Quigley, A. J. (2023). Omnisense: Exploring novel input sensing and interaction techniques on mobile device with an omni-directional camera. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, page 530, New York, NY, USA. Association for Computing Machinery.
Yera, A., Muguerza, J., Arbelaitz, O., Perona, I., Keers, R. N., Ashcroft, D. M., Williams, R., Peek, N., Jay, C., and Vigo, M. (2019). Modelling the interactive behaviour of users with a medication safety dashboard in a primary care setting. In Proceedings of International Journal of Medical Informatics, pages 395–403. Elsevier.
Yeroshkin, O. and Sobecki, J. (2024). Recommendations user interface in web-based e-commerce systems. In Proceedings of Procedia Computer Science, pages 2874–2881. Elsevier.
Zaina, L., Prates, R. O., Silva, S. E. D., Choma, J., Valentim, N. M. C., Frigo, L. B., and de Lima Bicho, A. (2024). Grandihc-br 2025-2035 - gc7: Interaction with emerging technologies: An ecosystem integrating humans, technologies, and contexts. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems (IHC ’24), pages 1–21. ACM.
Zanker, M., Rook, L., and Jannach, D. (2019). Measuring the impact of online personalisation: Past, present and future. In Proceedings of International Journal of Human-Computer Studies, pages 160–168. Elsevier.
Zhang, B. and Sundar, S. S. (2019). Proactive vs. reactive personalization: Can customization of privacy enhance user experience? In Proceedings of International Journal of Human-Computer Studies, pages 86–99. Elsevier.
Zhang, R., Wang, S., Xie, T., Duan, S., and Chen, M. (2024). Dynamic user interface generation for enhanced human-computer interaction using variational autoencoders. In 2024 4th International Conference on Communication Technology and Information Technology (ICCTIT), pages 260–265. IEEE.
Zhao, S. and Cheng, S. (2023). Adaptive navigation assistance based on eye movement features in virtual reality. In Proceedings of Virtual Reality and Intelligent Hardware, pages 232–248. Elsevier.
Çagla Çığ Karaman and Sezgin, T. M. (2018). Gaze-based predictive user interfaces: Visualizing user intentions in the presence of uncertainty. In Proceedings of International Journal of Human-Computer Studies, pages 78–91. Elsevier.
Ali, M., Khan, S., and Mashkoor, A. (2024). A conceptual framework for context-driven self-adaptive intelligent user interface based on android. In Proceedings of Cognition, Technology Work, pages 83–106. Springer.
Awada, I. A., Mocanu, I., Nastac, D.-I., Benta, D., and Radu, S. (2018). Adaptive user interface for healthcare application for people with dementia. In 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet), pages 1–5. IEEE.
Carrera-Rivera, A., Larrinaga, F., and Lasa, G. (2024). Adaptui: A framework for the development of adaptive user interfaces in smart product-service systems. In Proceedings of User Modeling and User-Adapted Interaction, pages 1929–1980. Springer.
Carrera-Rivera, A., Reguera-Bakhache, D., and Larrinaga, F. (2023). Structured dataset of human-machine interactions enabling adaptive user interfaces. In Proceedings of Scientific Data, page 831. Springer Nature.
Cherukuri, B. R. (2024). Development of design patterns with adaptive user interface for cloud native microservice architecture using deep learning with iot. In 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), pages 1866–1871. IEEE.
Chew, J. Y., Kawamoto, M., Okuma, T., Yoshida, E., and Kato, N. (2021). Multi-modal approach to evaluate adaptive visual stimuli of remote operation system using gaze behavior. In Proceedings of International Journal of Industrial Ergonomics, page 103223. Elsevier.
Conati, C., Barral, O., Putnam, V., and Rieger, L. (2021). Toward personalized xai: A case study in intelligent tutoring systems. In Proceedings of Artificial Intelligence, page 103503. Elsevier.
Costa, A., Silva, F., and Moreira, J. J. (2024). Towards an ai-driven user interface design for web applications. In Proceedings of Procedia Computer Science, pages 179–186. Elsevier.
Diego-Mas, J. A., Garzon-Leal, D., Poveda-Bautista, R., and Alcaide-Marzal, J. (2019). User-interfaces layout optimization using eye-tracking, mouse movements and genetic algorithms. In Proceedings of Applied Ergonomics, pages 197–209. Elsevier.
ElSayed, N., Veas, E., and Schmalstieg, D. (2024). Agents of mask: Mobile analytics from situated knowledge. In Interactions, pages 48–55, New York, NY, USA. Association for Computing Machinery.
Finne, R., Larsson, L., Mylonopoulou, V., Andreasson, S., Hjelm, T., Rost, M., Weilenmann, A., and Torgersson, O. (2022). Reversed multi-layer design as an approach to designing for digital seniors. In Nordic Human-Computer Interaction Conference, page 5, New York, NY, USA. Association for Computing Machinery.
Gomi, R., Takashima, K., Onishi, Y., Fujita, K., and Kitamura, Y. (2023). Ubisurface: A robotic touch surface for supporting mid-air planar interactions in room-scale vr. In Proceedings of ACM Human-Computer Interaction, page 443, New York, NY, USA. Association for Computing Machinery.
Grua, E. M., De Sanctis, M., Malavolta, I., Hoogendoorn, M., and Lago, P. (2022). An evaluation of the effectiveness of personalization and self-adaptation for e-health apps. In Proceedings of Information and Software Technology, page 106841. Elsevier.
Iglesias, A., Yue, T., Arellano, C., Ali, S., and Sagardui, G. (2018). Model-based personalized visualization system for monitoring evolving industrial cyber-physical system. In 2018 25th Asia-Pacific Software Engineering Conference (APSEC), pages 532–541. IEEE.
Iqbal, M. W., Ch, N. A., Shahzad, S. K., Naqvi, M. R., Khan, B. A., and Ali, Z. (2021). User context ontology for adaptive mobile-phone interfaces. IEEE Access, 9:96751–96762.
Jo, S.-M. and Cho, S.-B. (2019). A personalized context-aware soft keyboard adapted by random forest trained with additional data of same cluster. In Proceedings of Neurocomputing, pages 17–27. Elsevier.
Khamaj, A. and Ali, A. M. (2024). Adapting user experience with reinforcement learning: Personalizing interfaces based on user behavior analysis in real-time. In Proceedings of Alexandria Engineering Journal, pages 164–173. Elsevier.
Khan, M. and Khusro, S. (2023). Towards the design of personalized adaptive user interfaces for smart tv viewers. In Proceedings of Journal of King Saud University - Computer and Information Sciences, page 101777. Elsevier.
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., and Linkman, S. (2009). Systematic literature reviews in software engineering – a systematic literature review. Information and Software Technology, 51(1):7–15.
Kolekar, S. V., Pai, R. M., and Pai M.M., M. (2018). Adaptive user interface for moodle based e-learning system using learning styles. In Proceedings of Procedia Computer Science, pages 606–615. Elsevier.
Laguna Salvadó, L., Villeneuve, E., Masson, D., Abi Akle, A., and Bur, N. (2022). Decision support system for technology selection based on multi-criteria ranking: Application to nzeb refurbishment. In Proceedings of Building and Environment, page 108786. Elsevier.
Lu, F., Chen, M., Hsu, H., Deshpande, P., Wang, C. Y., and MacIntyre, B. (2024). Adaptive content placement in mixed reality through empirical user behavioral patterns. In 2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pages 199–204. IEEE.
Madduri, M. M., Burden, S. A., and Orsborn, A. L. (2023). Biosignal-based co-adaptive user-machine interfaces for motor control. In Proceedings of Current Opinion in Biomedical Engineering, page 100462. Elsevier.
Manoharan, S. K., Megalingam, R. K., and Lazar, A. M. A. (2024). Reconfgurable ui/ux design for robotic applications based on user experience. In 2024 4th Asian Conference on Innovation in Technology (ASIANCON), pages 1–6. IEEE.
Melchiorre, A. B., Penz, D., Ganhör, C., Lesota, O., Fragoso, V., Friztl, F., Parada-Cabaleiro, E., Schubert, F., and Schedl, M. (2022). Emomtb: Emotion-aware music tower blocks. In Proceedings of the 2022 International Conference on Multimedia Retrieval, pages 206–210, New York, NY, USA. Association for Computing Machinery.
Miraz, M. H., Ali, M., Excell, P. S., and Khan, S. (2021). Ai-based culture independent pervasive m-learning prototype using ui plasticity design. In Proceedings of Computers, Materials and Continua, pages 1021–1039. Tech Science Press.
Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. (2010). Preferred reporting items for systematic reviews and meta-analyses: The prisma statement. In Proceedings of the International Journal of Surgery, pages 336–341. Elsevier.
Märtin, C. and Herdin, C. (2024). Enabling real-time adaptations for individualized customer experience in user-centered e-business applications. In Proceedings of Procedia Computer Science, pages 1425–1432. Elsevier.
Okopnyi, P., Nordberg, O. E., and Guribye, F. (2024). Against generative ui. In Proceedings of the Halfway to the Future Symposium, page 12, New York, NY, USA. Association for Computing Machinery.
Pereira, R., Darin, T., and Silveira, M. S. (2024). Grandihc-br: Grand research challenges in human-computer interaction in brazil for 2025-2035. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, IHC ’24, New York, NY, USA. Association for Computing Machinery.
Pfeuffer, K. and Li, Y. (2018). Analysis and modeling of grid performance on touchscreen mobile devices. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pages 1–12, New York, NY, USA. Association for Computing Machinery.
Rathnayake, N., Meedeniya, D., Perera, I., and Welivita, A. (2019). A framework for adaptive user interface generation based on user behavioural patterns. In 2019 Moratuwa Engineering Research Conference (MERCon), pages 698–703. IEEE.
Rei, D., Clavel, C., Martin, J.-C., and Ravenet, B. (2024). Adapting goals and motivational messages on smartphones for motivation to walk. In Proceedings of Smart Health, page 100482. Elsevier.
Romero, O. J., Haig, A., Kirabo, L., Yang, Q., Zimmerman, J., Tomasic, A., and Steinfeld, A. (2020). A long-term evaluation of adaptive interface design for mobile transit information. In 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services, page 39, New York, NY, USA. Association for Computing Machinery.
Silva, K. G. G. H., Abeyasekare, W. A. P. S., Dasanayake, D. M. H. E., Nandisena, T. B., Kasthurirathna, D., and Kugathasan, A. (2021). Dynamic user interface personalization based on deep reinforcement learning. In 2021 3rd International Conference on Advancements in Computing (ICAC), pages 25–30. IEEE.
Smereka, M., Kołaczek, G., Sobecki, J., and Wasilewski, A. (2023). Adaptive user interface for workfow-erp system. In Proceedings of Procedia Computer Science, pages 2381–2391. Elsevier.
Sobecki, J., Wasilewski, A., and Kołaczek, G. (2020). Self-adaptation of workfow business software to the user’s requirements and behavior. In Proceedings of Procedia Computer Science, pages 3506–3513. Elsevier.
Sun, Q., Xue, Y., and Song, Z. (2024). Adaptive user interface generation through reinforcement learning: A data-driven approach to personalization and optimization. In 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC), pages 1386–1391. IEEE.
Suryani, M., Sensuse, D., and Santoso, H. (2024). An initial user model design for adaptive interface development in learning management system based on cognitive load. In Proceedings of Cognition, Technology Work, pages 653–672. Springer.
Wang, J. and Li, J. (2024). Human body features recognition based adaptive user interface for extra-large touch screens. In Proceedings of Displays, page 102838. Elsevier.
Wang, W., Gai, W., Zang, C., Bao, X., and Yang, C. (2023). Personalized recommendation of user interfaces based on foot interaction. In 2023 IEEE Smart World Congress (SWC), pages 1–8. IEEE.
Wang, W., Khalajzadeh, H., and Grundy, J. (2024a). Adaptive user interfaces in systems targeting chronic disease: A systematic literature review. In Proceedings of User Modeling and User-Adapted Interaction, pages 853–920. Springer.
Wang, W., Khalajzadeh, H., Grundy, J., Madugalla, A., and Obie, H. O. (2024b). Adaptive user interfaces for software supporting chronic disease. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Society, pages 118–129, New York, NY, USA. Association for Computing Machinery.
Wu, J., Todi, K., Chan, J., Myers, B. A., and Lafreniere, B. (2024). Framekit: A tool for authoring adaptive uis using keyframes. In Proceedings of the 29th International Conference on Intelligent User Interfaces, pages 660–674, New York, NY, USA. Association for Computing Machinery.
Yeo, H.-S., Wu, E., Kim, D., Lee, J., il Kim, H., Oh, S. Y., Takagi, L., Woo, W., Koike, H., and Quigley, A. J. (2023). Omnisense: Exploring novel input sensing and interaction techniques on mobile device with an omni-directional camera. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, page 530, New York, NY, USA. Association for Computing Machinery.
Yera, A., Muguerza, J., Arbelaitz, O., Perona, I., Keers, R. N., Ashcroft, D. M., Williams, R., Peek, N., Jay, C., and Vigo, M. (2019). Modelling the interactive behaviour of users with a medication safety dashboard in a primary care setting. In Proceedings of International Journal of Medical Informatics, pages 395–403. Elsevier.
Yeroshkin, O. and Sobecki, J. (2024). Recommendations user interface in web-based e-commerce systems. In Proceedings of Procedia Computer Science, pages 2874–2881. Elsevier.
Zaina, L., Prates, R. O., Silva, S. E. D., Choma, J., Valentim, N. M. C., Frigo, L. B., and de Lima Bicho, A. (2024). Grandihc-br 2025-2035 - gc7: Interaction with emerging technologies: An ecosystem integrating humans, technologies, and contexts. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems (IHC ’24), pages 1–21. ACM.
Zanker, M., Rook, L., and Jannach, D. (2019). Measuring the impact of online personalisation: Past, present and future. In Proceedings of International Journal of Human-Computer Studies, pages 160–168. Elsevier.
Zhang, B. and Sundar, S. S. (2019). Proactive vs. reactive personalization: Can customization of privacy enhance user experience? In Proceedings of International Journal of Human-Computer Studies, pages 86–99. Elsevier.
Zhang, R., Wang, S., Xie, T., Duan, S., and Chen, M. (2024). Dynamic user interface generation for enhanced human-computer interaction using variational autoencoders. In 2024 4th International Conference on Communication Technology and Information Technology (ICCTIT), pages 260–265. IEEE.
Zhao, S. and Cheng, S. (2023). Adaptive navigation assistance based on eye movement features in virtual reality. In Proceedings of Virtual Reality and Intelligent Hardware, pages 232–248. Elsevier.
Çagla Çığ Karaman and Sezgin, T. M. (2018). Gaze-based predictive user interfaces: Visualizing user intentions in the presence of uncertainty. In Proceedings of International Journal of Human-Computer Studies, pages 78–91. Elsevier.
Published
2025-09-08
How to Cite
SILVA, João Vitor D.; S. NUNES, Eunice P.; DE SOUZA, Patrícia C.; MACIEL, Cristiano; BORGES, Luciana C. L. F..
Personalization of Adaptive Interfaces Based on User Data Analysis: A Systematic Review. In: BRAZILIAN SYMPOSIUM ON HUMAN FACTORS IN COMPUTATIONAL SYSTEMS (IHC), 24. , 2025, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1466-1489.
DOI: https://doi.org/10.5753/ihc.2025.10911.
