Publication: Sistemas de recomendación y explicaciones basados en grafos de interacción
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2022-03-29
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Universidad Complutense de Madrid
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Abstract
En la sociedad actual, con la utilización de nuevas tecnologías en todos los ámbitos de nuestras vidas, el consumo en Internet ha avanzado a pasos agigantados. Los usuarios pueden encontrar infinidad de productos que consumir en cualquier ámbito, especialmente a través del comercio electrónico, las redes sociales y el entretenimiento en streaming. Determinar qué productos son los más adecuados según sus necesidades y preferencias puede llegar a ser una tarea complicada debido a la amplitud de la oferta de productos que se pueden encontrar en estas plataformas. Los sistemas de recomendación surgen para resolver este problema, facilitando el proceso de búsqueda y de toma de decisiones. no obstante, cuando los usuarios no confían en el sistema, los sistemas de recomendación no son tan útiles como cabría esperar...
Nowadays, with new technologies in all areas of our lives, Internet consumption has advanced quickly. Users can find an infinite number of products to consume in any field, especially through e-commerce, social networks and streaming entertainment. Determining which products are the most suitable according to their needs and preferencies can become a complicated tasd due to the breadth of product offerings that can be found on these platforms. Recommendation systems emerge to solve this problem, facilitating the search and decision-making process. However, when users do not trust the system, recommender systems are not as useful as one might expect because they do not understand how the sustems has concluded that a particular product in suitable for them...
Nowadays, with new technologies in all areas of our lives, Internet consumption has advanced quickly. Users can find an infinite number of products to consume in any field, especially through e-commerce, social networks and streaming entertainment. Determining which products are the most suitable according to their needs and preferencies can become a complicated tasd due to the breadth of product offerings that can be found on these platforms. Recommendation systems emerge to solve this problem, facilitating the search and decision-making process. However, when users do not trust the system, recommender systems are not as useful as one might expect because they do not understand how the sustems has concluded that a particular product in suitable for them...
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Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería de Software e Inteligencia Artificial, leída el 24-01-2022