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Fecha
2023-06-16
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Association for Computing Machinery, Inc
Citas
Resumen
Recommender Systems have been developed for years to guide the interaction of the users with systems in very diverse domains where information overload exists aimed to help humans in decision making. In order to better support the humans, the more the system knows about the user, the more useful recommendations the user can receive. In this sense, there is a need to explore which are the intrinsic human aspects that should be taken into account in each case when building the user models that provide the personalization. Moreover, there is a need to define and apply methodologies, guidelines and frameworks to develop this kind of systems in order to tackle the challenges of current artificial intelligence applications including issues such as ethics, transparency, explainability and sustainability. For our research, we have chosen the psychomotor domain. To provide some insights into this problem, in this paper we present the research directions we are exploring to apply a human-centric approach when developing the iBAID (intelligent Basket AID) psychomotor system, which aims to recommend the physical activities and movements to perform when training in basketball, either to improve the technique, to recover from an injury or even to keep active when getting older.
Descripción
The registered version of this conference paper, first published in "Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 337–342).", is available online at the publisher's website: https://doi.org/10.1145/3563359.3596993
Categorías UNESCO
Palabras clave
recommender systems, psychomotor intelligent systems, humancentric systems, hybrid artificial intelligence
Citación
Portaz, M., Manjarrés, Á., & Santos, O. C. (2023, June). Towards human-centric psychomotor recommender systems. In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 337–342). https://doi.org/10.1145/3563359.3596993
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial