Propuesta de Métricas de Usuario para Definir Perfiles de Usuario

Alberto Gaspar; Jose Ignacio Panach; Miriam Gil; Verónica Romero

doi:10.65234/interaccion.124

Autores/as

Alberto Gaspar Universitat de València, Departament d’informàtica, Escola Tècnica Superior d’Enginyeria https://orcid.org/0000-0002-9236-4391
Jose Ignacio Panach Universitat de València, Departament d’informàtica, Escola Tècnica Superior d’Enginyeria https://orcid.org/0000-0002-7043-6227
Miriam Gil Universitat de València, Departament d’informàtica, Escola Tècnica Superior d’Enginyeria https://orcid.org/0000-0002-2987-1825
Verónica Romero Universitat de València, Departament d’informàtica, Escola Tècnica Superior d’Enginyeria https://orcid.org/0000-0002-1721-5732

DOI:

https://doi.org/10.65234/interaccion.124

Palabras clave:

Perfil de usuario, Métricas de usuario, Características de usuario, Adaptación

Resumen

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.

Referencias

Abdelrazek, M., Purificato, E., Boratto, L., & Luca, E. W. D. (2023). FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, 3165-3169. https://doi.org/10.1145/3539618.3591814 DOI: https://doi.org/10.1145/3539618.3591814

Abri, S., Abri, R., & Çetin, S. (2020). Group-based Personalization Using Topical User Profile. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2020, Genoa, Italy, July 12-18, 2020, 181-186. https://doi.org/10.1145/3386392.3399559 DOI: https://doi.org/10.1145/3386392.3399559

Alhijawi, B., Fraihat, S., & Awajan, A. (2023). Adaptable inheritance-based prediction model for multi-criteria recommender system. Multim. Tools Appl., 82(21), 32421-32442. https://doi.org/10.1007/s11042-023-14728-z DOI: https://doi.org/10.1007/s11042-023-14728-z

Berkovitch, O., Caduri, S., Kahlon, N., Efros, A., Caciularu, A., & Dagan, I. (2024). Identifying User Goals from UI Trajectories. CoRR, abs/2406.14314. https://doi.org/10.48550/arXiv.2406.14314 DOI: https://doi.org/10.1145/3701716.3717525

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 Head Control and Flexible Numeric Tables for the Severely Disabled. Applied Sciences, 10(19). https://www.doi.org/10.3390/app10197005 DOI: https://doi.org/10.3390/app10197005

Dumitrescu, A., & Santini, S. (2021). Full coverage of a reader’s interests in context-based information filtering. J. Assoc. Inf. Sci. Technol., 72(8), 1011-1027. https://doi.org/10.1002/asi.24470 DOI: https://doi.org/10.1002/asi.24470

Gaspar, A., Gil, M., Panach, J. I., & Romero, V. (2024). Towards a general user model to develop intelligent user interfaces. Multim. Tools Appl., 83(26), 67501-67534. https://doi.org/10.1007/s11042-024-18240-w DOI: https://doi.org/10.1007/s11042-024-18240-w

Gignac, G. E., & Zajenkowski, M. (2020). The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data. Intelligence, 80, 101449. https://doi.org/10.1016/j.intell.2020.101449 DOI: https://doi.org/10.1016/j.intell.2020.101449

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, challenges and future direction. Soc. Netw. Anal. Min., 13(1), 137. https://doi.org/10.1007/s13278-023-01146-0 DOI: https://doi.org/10.1007/s13278-023-01146-0

Gothe, S. V., Ghosh, S., Mani, S., Guggilla, B., Agarwal, A., & Sanchi, C. (2021). Language Detection Engine for Multilingual Texting on Mobile Devices. CoRR, abs/2101.03963. https://doi.org/10.48550/arXiv.2101.03963

Hawamdeh, M., Altınay, Z., Altınay, F., Arnavut, A., Ozansoy, K., & Adamu, I. (2022). Comparative analysis of students and faculty level of awareness and knowledge of digital citizenship practices in a distance learning environment: Case study. Education and Information Technologies, 27(5), 6037-6068. https://doi.org/10.1007/s10639-021-10868-7 DOI: https://doi.org/10.1007/s10639-021-10868-7

Hoi, N. T., & Que, T. D. (2024). Discovering User Interest in Social Media Based on Correlation. Advances in Information and Communication Technology, 403-413. https://www.doi.org/10.1007/978-3-031-50818-9_43 DOI: https://doi.org/10.1007/978-3-031-50818-9_43

Hou, W., Liu, J., & Yan, X. (2021). A Questionnaire Data Clustering Method Based on Optimized K-Modes Algorithm. Artificial Intelligence in HCI - Second International Conference, AI-HCI 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24-29, 2021, Proceedings, 12797, 326-342. https://doi.org/10.1007/978-3-030-77772-2_22 DOI: https://doi.org/10.1007/978-3-030-77772-2_22

Hu, S., Kumar, A., Al-Turjman, F., Gupta, S., Seth, S., & Shubham. (2020). Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation. IEEE Access, 8, 26172-26189. https://doi.org/10.1109/ACCESS.2020.2971087 DOI: https://doi.org/10.1109/ACCESS.2020.2971087

Iqbal, M. W., Ahmad, N., Shahzad, S. K., Naqvi, M. R., Khan, B. A., & Ali, Z. (2021). User Context Ontology for Adaptive Mobile-Phone Interfaces. IEEE Access, 9, 96751-96762. https://doi.org/10.1109/ACCESS.2021.3095300 DOI: https://doi.org/10.1109/ACCESS.2021.3095300

Jarwal, M. K., Barun, A., Singh, A., & Srivastava, A. (2022). Mobile Application based Tracking using GPS and GSM. 2022 8th International Conference on Signal Processing and Communication (ICSC), 153-156. https://doi.org/10.1109/ICSC56524.2022.10009250 DOI: https://doi.org/10.1109/ICSC56524.2022.10009250

Jin, Y., Tintarev, N., Htun, N. N., & Verbert, K. (2020). Effects of personal characteristics in control-oriented user interfaces for music recommender systems. User Model. User Adapt. Interact., 30(2), 199-249. https://doi.org/10.1007/s11257-019-09247-2 DOI: https://doi.org/10.1007/s11257-019-09247-2

Kinjo, H., & Mbaitiga, Z. (2022). Development of flexible Text Input Device Based on Image Processing for Each Level of Disability Person. 2022 7th International Conference on Intelligent Informatics and Biomedical Science (ICIIBMS), 7, 378-379. https://www.doi.org/10.1109/ICIIBMS55689.2022.9971506 DOI: https://doi.org/10.1109/ICIIBMS55689.2022.9971506

Kubanyi, J., Hlavac, P., Simko, J., & Bielikova, M. (2018). Towards Automated Web Navigation and Search Skill Assessment: An Eye-tracking Study on the Skill Differences. 2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), 49-54. https://www.doi.org/10.1109/SMAP.2018.8501883 DOI: https://doi.org/10.1109/SMAP.2018.8501883

Laor, T., Mehanna, N., Durey, A., Dyadyuk, V., Laperdrix, P., Maurice, C., Oren, Y., Rouvoy, R., Rudametkin, W., & Yarom, Y. (2022). DRAWN APART : A Device Identification Technique based on Remote GPU Fingerprinting. Proceedings 2022 Network and Distributed System Security Symposium. https://doi.org/10.14722/ndss.2022.24093 DOI: https://doi.org/10.14722/ndss.2022.24093

Machová, K., Szabóova, M., Paralič, J., & Mičko, J. (2023). Detection of emotion by text analysis using machine learning. Frontiers in Psychology, 14. https://www.doi.org/10.3389/fpsyg.2023.1190326 DOI: https://doi.org/10.3389/fpsyg.2023.1190326

Mangat, G., & Sharma, S. (2023). How does the use of different targeted literature review (TLR) methodologies impact the research output? Parexel International. https://www.ispor.org/docs/default-source/intl2023/ispor23gsinghmsr36poster-pdf.pdf DOI: https://doi.org/10.1016/j.jval.2023.03.1572

Mishra, V., Laperdrix, P., Vastel, A., Rudametkin, W., Rouvoy, R., & Lopatka, M. (2020). Don’t Count Me Out: On the Relevance of IP Address in the Tracking Ecosystem. WWW ’20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 808-815. https://doi.org/10.1145/3366423.3380161 DOI: https://doi.org/10.1145/3366423.3380161

Oukfif, K., Battou, F., & Bouzefrane, S. (2020). Budget-Aware Performance Optimization of Workflows in Multiple Data Center Clouds. Mobile, Secure, and Programmable Networking - 6th International Conference, MSPN 2020, Paris, France, October 28-29, 2020, Revised Selected Papers, 12605, 144-160. https://doi.org/10.1007/978-3-030-67550-9_10 DOI: https://doi.org/10.1007/978-3-030-67550-9_10

Paulino, D., Ferreira, J., Netto, A., Correia, A., Ribeiro, J., Guimarães, D., Barroso, J., & Paredes, H. (2024). Probing into the Usage of Task Fingerprinting in Web Games to Enhance Cognitive Personalization: A Pilot Gamified Experience with Neurodivergent Participants. 2024 IEEE 12th International Conference on Serious Games and Applications for Health (SeGAH), 1-8. https://doi.org/10.1109/SeGAH61285.2024.10639597 DOI: https://doi.org/10.1109/SeGAH61285.2024.10639597

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 29th ACM International Conference on Information & Knowledge Management, 2189-2192. https://doi.org/10.1145/3340531.3412090 DOI: https://doi.org/10.1145/3340531.3412090

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 DOI: https://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 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), 1-6. https://www.doi.org/10.1109/IC3SIS54991.2022.9885610 DOI: https://doi.org/10.1109/IC3SIS54991.2022.9885610

Salem, I. E., & Al-Saedi, K. H. (2023). Intensive Malware Detection Approach based on Data Mining. Journal of Applied Engineering and Technological Science (JAETS), 5(1), 414-424. https://www.doi.org/10.37385/jaets.v5i1.2865 DOI: https://doi.org/10.37385/jaets.v5i1.2865

Sandoval-Gaytan, B. D., Camacho, L.-M., & Vazquez-Hurtado, C. (2023). Point Cloud Generation of Transparent Objects: A Comparison between Technologies. 2023 IEEE Global Engineering Education Conference (EDUCON), 1-3. https://doi.org/10.1109/EDUCON54358.2023.10125143 DOI: https://doi.org/10.1109/EDUCON54358.2023.10125143

Schmidt, A., Mayer, S., & Buschek, D. (2021). Introduction to Intelligent User Interfaces. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411763.3445021 DOI: https://doi.org/10.1145/3411763.3445021

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 DOI: 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 Trans. Mob. Comput., 19(2), 245-261. https://doi.org/10.1109/TMC.2019.2893253 DOI: https://doi.org/10.1109/TMC.2019.2893253

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 Users with Disabilities in User-Based Testing. Applied Sciences, 13(9). https://www.doi.org/10.3390/app13095498 DOI: https://doi.org/10.3390/app13095498

Sun, B., Mao, H., & Yin, C. (2020). Male and Female Users’ Differences in Online Technology Community Based on Text Mining. Frontiers in Psychology, 11. https://www.doi.org/10.3389/fpsyg.2020.00806 DOI: https://doi.org/10.3389/fpsyg.2020.00806

Tufail, S., Riggs, H., Tariq, M., & Sarwat, A. I. (2023). Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms. Electronics, 12(8). https://www.doi.org/10.3390/electronics12081789 DOI: https://doi.org/10.3390/electronics12081789

Vandic, D., Nederstigt, L. J., Frasincar, F., Kaymak, U., & Ido, E. (2024). A framework for approximate product search using faceted navigation and user preference ranking. Data Knowl. Eng., 149, 102241. https://doi.org/10.1016/j.datak.2023.102241 DOI: https://doi.org/10.1016/j.datak.2023.102241

Wang, B., & Liu, J. (2023). Characterizing and Early Predicting User Performance for Adaptive Search Path Recommendation. Proceedings of the Association for Information Science and Technology, 60(1), 408-420. https://doi.org/10.1002/pra2.799 DOI: https://doi.org/10.1002/pra2.799

Wu, D., Du, X., & Peng, F. (2024). Multi-layer and multi-source features stacking ensemble learning for user profile. Electric Power Systems Research, 229, 110128. https://doi.org/10.1016/j.epsr.2024.110128 DOI: https://doi.org/10.1016/j.epsr.2024.110128

Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., & Dietze, S. (2018). Predicting User Knowledge Gain in Informational Search Sessions. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018, 75-84. https://doi.org/10.1145/3209978.3210064 DOI: https://doi.org/10.1145/3209978.3210064

Yu, R., Tang, R., Rokicki, M., Gadiraju, U., & Dietze, S. (2021). Topic-independent modeling of user knowledge in informational search sessions. Inf. Retr. J., 24(3), 240-268. https://doi.org/10.1007/s10791-021-09391-7 DOI: https://doi.org/10.1007/s10791-021-09391-7

Zhou, J., Zahiri, S. M., Hughes, S., Jadda, K. A., Kallumadi, S., & Agichtein, E. (2021). De-Biased Modeling of Search Click Behavior with Reinforcement Learning. SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021, 1637-1641. https://doi.org/10.1145/3404835.3463228 DOI: https://doi.org/10.1145/3404835.3463228

Ziosi, M., Hewitt, B., Juneja, P., Taddeo, M., & Floridi, L. (2024). Smart cities: Reviewing the debate about their ethical implications. AI Soc., 39(3), 1185-1200. https://doi.org/10.1007/s00146-022-01558-0 DOI: https://doi.org/10.1007/s00146-022-01558-0

Propuesta de Métricas de Usuario para Definir Perfiles de Usuario

Autores/as

DOI:

Palabras clave:

Resumen

Referencias

Descargas

Publicado

Número

Sección

Licencia