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Propuesta de Métricas de Usuario para Definir Perfiles de Usuario

  • Alberto Gaspar [1] ; Jose Ignacio Panach [1] ; Miriam Gil [1] ; Verónica Romero [1]
    1. [1] Universitat de València

      Universitat de València

      Valencia, España

  • Localización: Interacción: Revista Digital de AIPO, ISSN-e 2695-6578, Vol. 6, Nº. 1, 2025, págs. 37-50
  • Idioma: español
  • DOI: 10.65234/interaccion.124
  • Títulos paralelos:
    • Proposal of User Metrics to Define User Profiles
  • Enlaces
  • Resumen
    • español

      La adaptación en tiempo de ejecución de los elementos gráficos de la interfaz de usuario es fundamental en el desarrollo de interfaces inteligentes. Para lograrlo, el sistema debe identificar las características del usuario de manera precisa y no intrusiva, permitiendo establecer su perfil y garantizando una interacción más cómoda y segura. Este trabajo propone métricas para recopilar información del usuario y definir perfiles en tiempo de ejecución, basadas en un modelo previamente establecido. Las métricas, extraídas de investigaciones previas, han sido clasificadas en automáticas y manuales según si sus parámetros pueden obtenerse en tiempo real. Como contribución, se han definido los parámetros necesarios para estimar las características clave de cada métrica.

      Además, se presenta un caso práctico para ilustrar su funcionamiento y un experimento con usuarios reales para evaluar su grado de coincidencia con los resultados esperados por los sujetos. Este estudio representa un avance hacia una metodología para diseñar sistemas interfaces de usuario inteligentes capaces de ajustarse dinámicamente al comportamiento y acciones del usuario en tiempo de ejecución.

    • English

      The real-time adaptation of graphical elements in the user interface is essential for the development of intelligent interfaces. To achieve this, the system must identify user characteristics in a precise and non-intrusive manner, allowing for the establishment of a user profile and ensuring a more comfortable and secure interaction. This study proposes metrics for collecting user information and defining profiles in real-time, based on a previously established model. The metrics, derived from previous research, have been classified as either automatic or manual, depending on whether their parameters can be obtained in real-time. As a contribution, the necessary parameters have been defined to estimate the key characteristics of each metric. Additionally, a practical case study is presented to illustrate its functionality, along with an experiment involving real users to assess the degree of alignment between their results and the expected outcomes. This study represents progress towards a methodology for designing intelligent user interfaces capable of dynamically adjusting to user behavior and actions in real-time.

  • Referencias bibliográficas
    • Abdelrazek, M., Purificato, E., Boratto, L., & Luca, E. W. D. (2023). FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User...
    • Abri, S., Abri, R., & Çetin, S. (2020). Group-based Personalization Using Topical User Profile. Adjunct Publication of the 28th ACM Conference...
    • Alhijawi, B., Fraihat, S., & Awajan, A. (2023). Adaptable inheritance-based prediction model for multi-criteria recommender system. Multim....
    • Berkovitch, O., Caduri, S., Kahlon, N., Efros, A., Caciularu, A., & Dagan, I. (2024). Identifying User Goals from UI Trajectories. CoRR, abs/2406.14314....
    • Chang, C.-M., Lin, C.-S., Chen, W.-C., Chen, C.-T., & Hsu, Y.-L. (2020). Development and Application of a Human–Machine Interface Using...
    • Dumitrescu, A., & Santini, S. (2021). Full coverage of a reader’s interests in context-based information filtering. J. Assoc. Inf. Sci....
    • Gaspar, A., Gil, M., Panach, J. I., & Romero, V. (2024). Towards a general user model to develop intelligent user interfaces. Multim....
    • Gignac, G. E., & Zajenkowski, M. (2020). The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the...
    • Gilbert, J., Hamid, S., Hashem, I. A. T., Ghani, N. B. A., & Boluwatife, F. F. (2023). The rise of user profiling in social media: Review,...
    • Gothe, S. V., Ghosh, S., Mani, S., Guggilla, B., Agarwal, A., & Sanchi, C. (2021). Language Detection Engine for Multilingual Texting...
    • Hawamdeh, M., Altınay, Z., Altınay, F., Arnavut, A., Ozansoy, K., & Adamu, I. (2022). Comparative analysis of students and faculty level...
    • Hoi, N. T., & Que, T. D. (2024). Discovering User Interest in Social Media Based on Correlation. Advances in Information and Communication Technology,...
    • Hou, W., Liu, J., & Yan, X. (2021). A Questionnaire Data Clustering Method Based on Optimized K-Modes Algorithm. Artificial Intelligence...
    • Hu, S., Kumar, A., Al-Turjman, F., Gupta, S., Seth, S., & Shubham. (2020). Reviewer Credibility and Sentiment Analysis Based User Profile Modelling...
    • Iqbal, M. W., Ahmad, N., Shahzad, S. K., Naqvi, M. R., Khan, B. A., & Ali, Z. (2021). User Context Ontology for Adaptive Mobile-Phone...
    • Jarwal, M. K., Barun, A., Singh, A., & Srivastava, A. (2022). Mobile Application based Tracking using GPS and GSM. 2022 8th International Conference...
    • Jin, Y., Tintarev, N., Htun, N. N., & Verbert, K. (2020). Effects of personal characteristics in control-oriented user interfaces for...
    • Kinjo, H., & Mbaitiga, Z. (2022). Development of flexible Text Input Device Based on Image Processing for Each Level of Disability Person....
    • Kubanyi, J., Hlavac, P., Simko, J., & Bielikova, M. (2018). Towards Automated Web Navigation and Search Skill Assessment: An Eye-tracking...
    • Laor, T., Mehanna, N., Durey, A., Dyadyuk, V., Laperdrix, P., Maurice, C., Oren, Y., Rouvoy, R., Rudametkin, W., & Yarom, Y. (2022). DRAWN APART...
    • Machová, K., Szabóova, M., Paralič, J., & Mičko, J. (2023). Detection of emotion by text analysis using machine learning. Frontiers in...
    • Mangat, G., & Sharma, S. (2023). How does the use of different targeted literature review (TLR) methodologies impact the research output?...
    • Mishra, V., Laperdrix, P., Vastel, A., Rudametkin, W., Rouvoy, R., & Lopatka, M. (2020). Don’t Count Me Out: On the Relevance of IP Address...
    • Oukfif, K., Battou, F., & Bouzefrane, S. (2020). Budget-Aware Performance Optimization of Workflows in Multiple Data Center Clouds. Mobile, Secure,...
    • Paulino, D., Ferreira, J., Netto, A., Correia, A., Ribeiro, J., Guimarães, D., Barroso, J., & Paredes, H. (2024). Probing into the Usage...
    • Quantux. (2025). Quant-UX. https://app.quant-ux.com
    • Rao, N., Bansal, C., Mukherjee, S., & Maddila, C. (2020). Product Insights: Analyzing Product Intents in Web Search. Proceedings of the...
    • Rybaczewska, M., & Sparks, L. (2022). Ageing consumers and e-commerce activities. Ageing and Society, 42(8), 1879-1898. https://www.doi.org/10.1017/S0144686X20001932
    • Sajana, S. T., Jacob, S., P, V., Menon, V. G., & C, S. P. (2022). Context-aware gender and age recognition from smartphone sensors. 2022...
    • Salem, I. E., & Al-Saedi, K. H. (2023). Intensive Malware Detection Approach based on Data Mining. Journal of Applied Engineering and Technological...
    • Sandoval-Gaytan, B. D., Camacho, L.-M., & Vazquez-Hurtado, C. (2023). Point Cloud Generation of Transparent Objects: A Comparison between Technologies....
    • Schmidt, A., Mayer, S., & Buschek, D. (2021). Introduction to Intelligent User Interfaces. Extended Abstracts of the 2021 CHI Conference...
    • Sepulveda, G. K., Besoain, F., & Barriga, N. A. (2020). Exploring Dynamic Difficulty Adjustment in Videogames. CoRR, abs/2007.07220. https://www.doi.org/10.1109/CHILECON47746.2019.8988068)
    • Shi, S. W., & Trusov, M. (2021). The Path to Click: Are You on It? Mark. Sci., 40(2), 344-365. https://doi.org/10.1287/mksc.2020.1253
    • Sikder, A. K., Aksu, H., & Uluagac, A. S. (2020). A Context-Aware Framework for Detecting Sensor-Based Threats on Smart Devices. IEEE...
    • Singh, S. (2022). Emphasis on the Minimization of False Negatives or False Positives in Binary Classification. CoRR, abs/2204.02526. https://doi.org/10.48550/arXiv.2204.02526
    • Šumak, B., Kous, K., Martínez-Normand, L., Pekša, J., & Pušnik, M. (2023). Identification of Challenges and Best Practices for Including...
    • Sun, B., Mao, H., & Yin, C. (2020). Male and Female Users’ Differences in Online Technology Community Based on Text Mining. Frontiers...
    • Tufail, S., Riggs, H., Tariq, M., & Sarwat, A. I. (2023). Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries,...
    • Vandic, D., Nederstigt, L. J., Frasincar, F., Kaymak, U., & Ido, E. (2024). A framework for approximate product search using faceted navigation...
    • Wang, B., & Liu, J. (2023). Characterizing and Early Predicting User Performance for Adaptive Search Path Recommendation. Proceedings...
    • Wu, D., Du, X., & Peng, F. (2024). Multi-layer and multi-source features stacking ensemble learning for user profile. Electric Power Systems...
    • Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., & Dietze, S. (2018). Predicting User Knowledge Gain in Informational Search...
    • Yu, R., Tang, R., Rokicki, M., Gadiraju, U., & Dietze, S. (2021). Topic-independent modeling of user knowledge in informational search...
    • Zhou, J., Zahiri, S. M., Hughes, S., Jadda, K. A., Kallumadi, S., & Agichtein, E. (2021). De-Biased Modeling of Search Click Behavior...
    • Ziosi, M., Hewitt, B., Juneja, P., Taddeo, M., & Floridi, L. (2024). Smart cities: Reviewing the debate about their ethical implications....
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