DESARROLLO DE MÉTODOS DE EXPLICABILIDAD PARA SISTEMAS DE RECOMENDACIÓN GRUPAL
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2023-10-03
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Este trabajo de fin de máster se centra en el desarrollo de métodos de explicabilidad para sistemas de
recomendación grupal. El objetivo principal es proporcionar explicaciones claras y comprensibles sobre las
recomendaciones generadas en un contexto grupal, donde múltiples usuarios están involucrados y se consideran
las preferencias y restricciones del grupo. El enfoque metodológico se basa en una revisión de la literatura
existente sobre técnicas de recomendación y explicabilidad en sistemas de recomendación. A partir de esta
revisión, se diseñarán y desarrollarán métodos de explicabilidad adaptados para sistemas de recomendación
grupal. Además, se llevarán a cabo experimentos y análisis comparativos para evaluar la efectividad y calidad de
los métodos de explicabilidad desarrollados. Los resultados obtenidos permitirán determinar la eficacia de los
métodos propuestos y su capacidad para generar explicaciones comprensibles y relevantes para los usuarios del
sistema
This master's thesis addresses the challenge of providing explainability in group recommendation systems. The main objective is to deliver clear and comprehensible explanations for recommendations generated within a group context, while considering the preferences and constraints of multiple users. To achieve this goal, an extensive literature review is conducted to identify relevant approaches and methodologies in recommendation techniques and explainability. Building upon this review, tailored explainability methods specifically adapted for group recommendation systems are designed and implemented. Furthermore, experiments and comparative analyses are performed to evaluate the effectiveness and quality of the developed explainability methods. The results contribute to enhancing the understandability and trustworthiness of recommendations in group settings, fostering user satisfaction and engagement
This master's thesis addresses the challenge of providing explainability in group recommendation systems. The main objective is to deliver clear and comprehensible explanations for recommendations generated within a group context, while considering the preferences and constraints of multiple users. To achieve this goal, an extensive literature review is conducted to identify relevant approaches and methodologies in recommendation techniques and explainability. Building upon this review, tailored explainability methods specifically adapted for group recommendation systems are designed and implemented. Furthermore, experiments and comparative analyses are performed to evaluate the effectiveness and quality of the developed explainability methods. The results contribute to enhancing the understandability and trustworthiness of recommendations in group settings, fostering user satisfaction and engagement