Adaptive Interfaces: Exploring Machine Learning Algorithms for Personalizing User Experiences
Abstract
The personalization of user interfaces through machine learning algorithms is an increasingly relevant field in Human-Computer Interaction (HCI), aiming to develop systems that dynamically adapt to individual user needs and preferences. This study reports a Systematic Literature Mapping (SLM) that critically analyzed scientific studies to investigate current approaches. The analysis focused on the predominant machine learning algorithms, the methods used to collect user data, and the adaptation strategies applied to personalize the user experience. The findings provide a consolidated overview that contributes to the theoretical advancement of the field by identifying mature approaches, existing gaps, and research opportunities. Finally, the study highlights trends that can guide the development of future interfaces that are more effective, inclusive, and user-centered.
References
Baranauskas, M. C. C., de Souza, C. S., and Pereira, R. (2012). Grandihc-br: prospecção de grandes desafios de pesquisa em interação humano-computador no brasil. In Companion Proceedings of the 11th Brazilian Symposium on Human Factors in Computing Systems, pages 63–64.
Barbosa, S. and Silva, B. (2010). Interação humano-computador. Elsevier Brasil.
Basili, Rini (Original article, 1994 ed.), V., Caldiera (Original article, 1994 ed.), G., and Rombach (Original article, . e. (2002). Goal Question Metric (GQM) Approach. John Wiley & Sons, Ltd.
Bécue, A., Praça, I., and Gama, J. (2021). Artificial intelligence, cyber-threats and industry 4.0: Challenges and opportunities. Artificial Intelligence Review, 54(5):3849–3886.
Bouzit, S., Calvary, G., Coutaz, J., Chêne, D., Petit, E., and Vanderdonckt, J. (2017). The pda-lpa design space for user interface adaptation. In 11th IEEE International Conference on Research Challenges in Information Science (RCIS), pages 353–364. IEEE.
Browne, D. (2016). Adaptive user interfaces. Elsevier.
Canito, A., Mota, D., Marreiros, G., Corchado, J. M., and Martins, C. (2020). Contextual adaptative interfaces for industry 4.0. In International Symposium on Distributed Computing and Artificial Intelligence, pages 149–157. Springer.
Carrera-Rivera, A., Reguera-Bakhache, D., Larrinaga, F., and Lasa, G. (2024). Exploring the transformation of user interactions to adaptive human-machine interfaces. In Proceedings of the 29th International Conference on Intelligent User Interfaces. Association for Computing Machinery.
Chen, J., Ganguly, B., Kanade, S. G., and Duffy, V. G. (2023). Impact of ai on mobile computing: A systematic review from a human factors perspective. In Lecture Notes in Computer Science, volume 14059 LNCS of Lecture Notes in Computer Science, pages 24–38. Springer Science and Business Media Deutschland GmbH. Conference paper.
Da Silva, M. R., Leite, L. L., and Garcia Costa Dos Santos, G. L. (2022). Algorithms for the development of adaptive web interfaces: A systematic literature review. In 2022 IEEE World Conference on Engineering Education (EDUNINE), pages 01–08. IEEE.
Dudley, J. J. and Kristensson, P. O. (2018). A review of user interface design for interactive machine learning. ACM Transactions on Interactive Intelligent Systems (TiiS), 8(2):1–37.
El Misilmani, H. M. and Naous, T. (2019). Machine learning in antenna design: An overview on machine learning concept and algorithms. In 2019 International Conference on High Performance Computing & Simulation (HPCS), pages 600–607. IEEE.
Gao, J., Reddy, S., Berseth, G., Dragan, A. D., and Levine, S. (2023). Bootstrapping adaptive human-machine interfaces with offline reinforcement learning. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7523–7530. IEEE.
Gaspar-Figueiredo, D., Fernández-Diego, M., Nuredini, R., Abrahao, S., and Insfran, E. (2024). Reinforcement learning-based framework for the intelligent adaptation of user interfaces. In Proceedings of the 1st International Workshop on Intelligent User Interfaces for Software Engineering, pages 40–48. Association for Computing Machinery.
Göbel, F., Giannopoulos, I., and Raubal, M. (2016). The importance of visual attention for adaptive interfaces. In Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pages 930–935. Association for Computing Machinery.
Heck, M., Edinger, J., Bünemann, J., and Becker, C. (2021). Exploring gaze-based prediction strategies for preference detection in dynamic interface elements. In Proceedings of the 2021 International Conference on Advanced Visual Interfaces, pages 129–139. Association for Computing Machinery.
Hussain, J., Ul Hassan, A., Muhammad Bilal, H. S., Ali, R., Afzal, M., Hussain, S., Bang, J., Banos, O., and Lee, S. (2018). Model-based adaptive user interface based on context and user experience evaluation. Journal on Multimodal User Interfaces, 12:1–16.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Miraz, M. H., Ali, M., and Excell, P. S. (2021). Adaptive user interfaces and universal usability through plasticity of user interface design. Computer Science Review, 40:100363.
Oboko, R. O., Maina, E. M., Waiganjo, P. W., Omwenga, E. I., and Wario, R. D. (2016). Designing adaptive learning support through machine learning techniques. In 2016 IST-Africa Conference, IST-Africa 2016. Institute of Electrical and Electronics Engineers Inc. Conference paper.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studies in software engineering. Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (EASE), pages 68–77.
Petticrew, M. and Roberts, H. (2008). Systematic reviews in the social sciences: A practical guide. Systematic Reviews in the Social Sciences: A Practical Guide.
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.
Reguera-Bakhache, D., Garitano, I., Uribeetxeberria, R., Cernuda, C., and Zurutuza, U. (2020). Data-driven industrial human-machine interface temporal adaptation for process optimization. In 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), volume 1, pages 518–525. IEEE.
Santos, J., Martins, I., and Rodrigues, J. M. (2021). Framework for controlling knx devices based on gestures. In International Conference on Human-Computer Interaction, pages 507–518. Springer.
Shi, Z., Groechel, T. R., Jain, S., Chima, K., Rudovic, O., and Matari?, M. J. (2022). Toward personalized affect-aware socially assistive robot tutors for long-term interventions with children with autism. ACM Transactions on Human-Robot Interaction, 11. Cited by: 7.
Shneiderman, B. and Plaisant, C. (2010). Designing the user interface: strategies for effective human-computer interaction. Pearson Education India.
Todi, K., Bailly, G., Leiva, L., and Oulasvirta, A. (2021). Adapting user interfaces with model-based reinforcement learning. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–13.
Torok, L., Pelegrino, M., Trevisan, D., Montenegro, A., and Clua, E. (2017). Designing game controllers in a mobile device. In Lecture Notes in Computer Science, volume 10289 LNCS of Lecture Notes in Computer Science, pages 456–468. Springer Verlag. Conference paper.
Vanderdonckt, J., Bouzit, S., Calvary, G., and Chêne, D. (2019). Exploring a design space of graphical adaptive menus: Normal vs. small screens. ACM Transactions on Interactive Intelligent Systems, 10.
Wang, Y. (2022). Research on the construction of human-computer interaction system based on a machine learning algorithm. Journal of Sensors, 2022:1–11.
Wasilewski, A. and Kolaczek, G. (2024). One size does not fit all: Multivariant user interface personalization in e-commerce. IEEE Access, 12:65570–65582.
Xu, W., Dainoff, M. J., Ge, L., and Gao, Z. (2023). Transitioning to human interaction with ai systems: New challenges and opportunities for hci professionals to enable human-centered ai. International Journal of Human–Computer Interaction, 39(3):494–518.
Zhou, Z.-H. (2021). Machine learning. Springer nature.
Zhu, M., He, T., and Lee, C. (2020). Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems. Applied Physics Reviews, 7(3).
Barbosa, S. and Silva, B. (2010). Interação humano-computador. Elsevier Brasil.
Basili, Rini (Original article, 1994 ed.), V., Caldiera (Original article, 1994 ed.), G., and Rombach (Original article, . e. (2002). Goal Question Metric (GQM) Approach. John Wiley & Sons, Ltd.
Bécue, A., Praça, I., and Gama, J. (2021). Artificial intelligence, cyber-threats and industry 4.0: Challenges and opportunities. Artificial Intelligence Review, 54(5):3849–3886.
Bouzit, S., Calvary, G., Coutaz, J., Chêne, D., Petit, E., and Vanderdonckt, J. (2017). The pda-lpa design space for user interface adaptation. In 11th IEEE International Conference on Research Challenges in Information Science (RCIS), pages 353–364. IEEE.
Browne, D. (2016). Adaptive user interfaces. Elsevier.
Canito, A., Mota, D., Marreiros, G., Corchado, J. M., and Martins, C. (2020). Contextual adaptative interfaces for industry 4.0. In International Symposium on Distributed Computing and Artificial Intelligence, pages 149–157. Springer.
Carrera-Rivera, A., Reguera-Bakhache, D., Larrinaga, F., and Lasa, G. (2024). Exploring the transformation of user interactions to adaptive human-machine interfaces. In Proceedings of the 29th International Conference on Intelligent User Interfaces. Association for Computing Machinery.
Chen, J., Ganguly, B., Kanade, S. G., and Duffy, V. G. (2023). Impact of ai on mobile computing: A systematic review from a human factors perspective. In Lecture Notes in Computer Science, volume 14059 LNCS of Lecture Notes in Computer Science, pages 24–38. Springer Science and Business Media Deutschland GmbH. Conference paper.
Da Silva, M. R., Leite, L. L., and Garcia Costa Dos Santos, G. L. (2022). Algorithms for the development of adaptive web interfaces: A systematic literature review. In 2022 IEEE World Conference on Engineering Education (EDUNINE), pages 01–08. IEEE.
Dudley, J. J. and Kristensson, P. O. (2018). A review of user interface design for interactive machine learning. ACM Transactions on Interactive Intelligent Systems (TiiS), 8(2):1–37.
El Misilmani, H. M. and Naous, T. (2019). Machine learning in antenna design: An overview on machine learning concept and algorithms. In 2019 International Conference on High Performance Computing & Simulation (HPCS), pages 600–607. IEEE.
Gao, J., Reddy, S., Berseth, G., Dragan, A. D., and Levine, S. (2023). Bootstrapping adaptive human-machine interfaces with offline reinforcement learning. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7523–7530. IEEE.
Gaspar-Figueiredo, D., Fernández-Diego, M., Nuredini, R., Abrahao, S., and Insfran, E. (2024). Reinforcement learning-based framework for the intelligent adaptation of user interfaces. In Proceedings of the 1st International Workshop on Intelligent User Interfaces for Software Engineering, pages 40–48. Association for Computing Machinery.
Göbel, F., Giannopoulos, I., and Raubal, M. (2016). The importance of visual attention for adaptive interfaces. In Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pages 930–935. Association for Computing Machinery.
Heck, M., Edinger, J., Bünemann, J., and Becker, C. (2021). Exploring gaze-based prediction strategies for preference detection in dynamic interface elements. In Proceedings of the 2021 International Conference on Advanced Visual Interfaces, pages 129–139. Association for Computing Machinery.
Hussain, J., Ul Hassan, A., Muhammad Bilal, H. S., Ali, R., Afzal, M., Hussain, S., Bang, J., Banos, O., and Lee, S. (2018). Model-based adaptive user interface based on context and user experience evaluation. Journal on Multimodal User Interfaces, 12:1–16.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Miraz, M. H., Ali, M., and Excell, P. S. (2021). Adaptive user interfaces and universal usability through plasticity of user interface design. Computer Science Review, 40:100363.
Oboko, R. O., Maina, E. M., Waiganjo, P. W., Omwenga, E. I., and Wario, R. D. (2016). Designing adaptive learning support through machine learning techniques. In 2016 IST-Africa Conference, IST-Africa 2016. Institute of Electrical and Electronics Engineers Inc. Conference paper.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studies in software engineering. Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (EASE), pages 68–77.
Petticrew, M. and Roberts, H. (2008). Systematic reviews in the social sciences: A practical guide. Systematic Reviews in the Social Sciences: A Practical Guide.
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.
Reguera-Bakhache, D., Garitano, I., Uribeetxeberria, R., Cernuda, C., and Zurutuza, U. (2020). Data-driven industrial human-machine interface temporal adaptation for process optimization. In 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), volume 1, pages 518–525. IEEE.
Santos, J., Martins, I., and Rodrigues, J. M. (2021). Framework for controlling knx devices based on gestures. In International Conference on Human-Computer Interaction, pages 507–518. Springer.
Shi, Z., Groechel, T. R., Jain, S., Chima, K., Rudovic, O., and Matari?, M. J. (2022). Toward personalized affect-aware socially assistive robot tutors for long-term interventions with children with autism. ACM Transactions on Human-Robot Interaction, 11. Cited by: 7.
Shneiderman, B. and Plaisant, C. (2010). Designing the user interface: strategies for effective human-computer interaction. Pearson Education India.
Todi, K., Bailly, G., Leiva, L., and Oulasvirta, A. (2021). Adapting user interfaces with model-based reinforcement learning. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–13.
Torok, L., Pelegrino, M., Trevisan, D., Montenegro, A., and Clua, E. (2017). Designing game controllers in a mobile device. In Lecture Notes in Computer Science, volume 10289 LNCS of Lecture Notes in Computer Science, pages 456–468. Springer Verlag. Conference paper.
Vanderdonckt, J., Bouzit, S., Calvary, G., and Chêne, D. (2019). Exploring a design space of graphical adaptive menus: Normal vs. small screens. ACM Transactions on Interactive Intelligent Systems, 10.
Wang, Y. (2022). Research on the construction of human-computer interaction system based on a machine learning algorithm. Journal of Sensors, 2022:1–11.
Wasilewski, A. and Kolaczek, G. (2024). One size does not fit all: Multivariant user interface personalization in e-commerce. IEEE Access, 12:65570–65582.
Xu, W., Dainoff, M. J., Ge, L., and Gao, Z. (2023). Transitioning to human interaction with ai systems: New challenges and opportunities for hci professionals to enable human-centered ai. International Journal of Human–Computer Interaction, 39(3):494–518.
Zhou, Z.-H. (2021). Machine learning. Springer nature.
Zhu, M., He, T., and Lee, C. (2020). Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems. Applied Physics Reviews, 7(3).
Published
2025-07-01
How to Cite
SILVA, Felipe William Galdino da; ALEGRIA, Reyner Carlos Silva; SILVA, Lais Samily Xavier da; RODRIGUES, Andrey Antonio de Oliveira.
Adaptive Interfaces: Exploring Machine Learning Algorithms for Personalizing User Experiences. In: ICET TECHNOLOGY CONFERENCE (CONNECTECH), 2. , 2025, Itacoatiara/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 134-148.
DOI: https://doi.org/10.5753/connect.2025.12330.
