“Soy Cupido” Sistema de Recomendación de Citas Explicables
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2024-11-26
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Este trabajo desarrolla “Soy Cupido”, una aplicación web de citas con un sistema de recomendación
explicable que ofrece emparejamientos basados en intereses, preferencias y datos personales de los
usuarios. Su propósito es brindar recomendaciones personalizadas con explicaciones claras sobre la
compatibilidad, aumentando la confianza del usuario en el sistema. El desarrollo empleó metodologías
ágiles, utilizando React para el frontend y Flask para el backend, permitiendo un enfoque iterativo
centrado en el usuario. Se exploran conceptos de sistemas de recomendación y su evolución en el
contexto de las citas en línea, incluyendo técnicas como filtrado colaborativo y personalización
mediante aprendizaje automático. La solución incorpora una base de datos en memoria y un algoritmo
diseñado para optimizar la experiencia de usuario, asegurando seguridad, escalabilidad y usabilidad.
Se realizaron pruebas para validar el sistema y se sugieren mejoras para desarrollos futuros.
This work presents “Soy Cupido,” a web-based dating application featuring an explainable recommendation system that provides matches based on users’ interests, preferences, and personal data. Its objective is to offer personalized recommendations with transparent explanations regarding compatibility, thereby enhancing user trust in the system. The development process followed agile methodologies, leveraging React for the frontend and Flask for the backend, facilitating an iterative, user-centered approach. This work explores concepts related to recommendation systems and their evolution within the online dating context, including techniques such as collaborative filtering and machine learning-driven personalization. The solution integrates an in-memory database and a custom algorithm designed to optimize user experience while ensuring security, scalability, and usability. System validation tests were conducted, and recommendations for future enhancements are proposed.
This work presents “Soy Cupido,” a web-based dating application featuring an explainable recommendation system that provides matches based on users’ interests, preferences, and personal data. Its objective is to offer personalized recommendations with transparent explanations regarding compatibility, thereby enhancing user trust in the system. The development process followed agile methodologies, leveraging React for the frontend and Flask for the backend, facilitating an iterative, user-centered approach. This work explores concepts related to recommendation systems and their evolution within the online dating context, including techniques such as collaborative filtering and machine learning-driven personalization. The solution integrates an in-memory database and a custom algorithm designed to optimize user experience while ensuring security, scalability, and usability. System validation tests were conducted, and recommendations for future enhancements are proposed.