Interfaces Adaptativas: Explorando Algoritmos de Aprendizado de Máquina para a Personalização de Experiências do Usuário
Resumo
A personalização de interfaces de usuário via algoritmos de aprendizado de máquina é um campo de crescente interesse na Interação Humano-Computador (IHC), buscando criar sistemas que se adaptem dinamicamente às necessidades e preferências individuais dos usuários. Este trabalho reporta um Mapeamento Sistemático da Literatura (MSL) que analisou estudos científicos para investigar criticamente as abordagens existentes. A análise focou nos algoritmos de aprendizado de máquina predominantes, nos métodos utilizados para coletar dados dos usuários e nas estratégias de adaptação aplicadas para personalizar a experiência de uso. Os achados oferecem uma visão consolidada que contribui para o avanço teórico da área, identificando as abordagens mais maduras, lacunas existentes e oportunidades de pesquisa. Por fim, o estudo aponta tendências que podem guiar o desenvolvimento de futuras interfaces mais eficazes, inclusivas e centradas no usuário.
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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.
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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.
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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).
Publicado
01/07/2025
Como Citar
SILVA, Felipe William Galdino da; ALEGRIA, Reyner Carlos Silva; SILVA, Lais Samily Xavier da; RODRIGUES, Andrey Antonio de Oliveira.
Interfaces Adaptativas: Explorando Algoritmos de Aprendizado de Máquina para a Personalização de Experiências do Usuário. In: CONFERÊNCIA DE TECNOLOGIA DO ICET (CONNECTECH), 2. , 2025, Itacoatiara/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 134-148.
DOI: https://doi.org/10.5753/connect.2025.12330.
