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Recolector de Ciencia Abierta, RECOLECTA
Bachelor thesis . 2024
License: CC BY NC ND
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UCrea
Bachelor thesis . 2024
License: CC BY NC ND
Data sources: UCrea
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Asistente nutricional personalizado con inteligencia artificial generativa

Personalized nutritional assistant with generative artificial intelligence
Authors: Fernández Monagas, Arahí;

Asistente nutricional personalizado con inteligencia artificial generativa

Abstract

La alimentación es una necesidad básica para el ser humano y es un pilar fundamental tanto para el bienestar físico como para el mental. Adoptar una dieta adecuada puede ser una tarea compleja especialmente por la falta de tiempo y otras necesidades individuales de cada persona. Esto afecta considerablemente a la alimentación de las personas, llegando a no ser correcta en la mayoría de los casos. En respuesta a este desafío, el proyecto propone una solución innovadora que utiliza la Inteligencia Artificial Generativa para ofrecer un sistema nutricional personalizado y efectivo, permitiendo al usuario una experiencia eficiente para la gestión de su dieta y alcanzar sus objetivos de salud y bienestar. La aplicación desarrollada como parte de este proyecto ofrece diversas funcionalidades clave. Para implementar estas funcionalidades, se utilizan algoritmos de Inteligencia Artificial (IA) avanzados, como Large Language Models (LLMs), para comprender las preferencias del usuario, embeddings para representar la información nutricional, bases de datos vectoriales para almacenar información sobre alimentos y recetas, y, por último, Retrieval Augmented Generation (RAG), para generar los menús personalizados. Esta solución ofrece una experiencia personalizada y efectiva para la gestión de la dieta, permitiendo a los usuarios planificar comidas, controlar la ingesta calórica y alcanzar sus objetivos. La aplicación, llamada Salvia, combina Inteligencia Artificial (IA) de última generación con un enfoque personalizado para la gestión de la dieta, lo que la diferencia de las soluciones existentes en el mercado.

Eating is a basic necessity for humans and a fundamental pillar for both physical and mental well-being. Adopting an adequate diet can be a complex task, especially due to a lack of time and individual needs. This considerably affects people’s eating habits, often resulting in incorrect nutrition. In response to this challenge, the project proposes an innovative solution that utilizes generative artificial intelligence to offer a personalized and effective nutritional system, allowing users an efficient experience for managing their diet and achieving their health and wellness goals. The application developed as part of this project offers several key functionalities. To implement these functionalities, advanced Artificial Intelligence (AI) algorithms are used, such as Large Language Models (LLMs) to understand user preferences, embeddings to represent nutritional information, vector databases to store information about foods and recipes, and Retrieval Augmented Generation (RAG) to generate personalized menus. This solution offers a personalized and effective experience for diet management, allowing users to plan meals, control calorie intake, and achieve their goals. The application, called Salvia, combines state-of-the-art Artificial Intelligence (AI) with a personalized approach to diet management, setting it apart from existing solutions in the market.

Grado en Ingeniería Informática

Country
Spain
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Keywords

Procesamiento de lenguaje natural, Generative AI, Natural language processing, IA generativa, Modelo RAG, Nutrición de precisión, Precision nutrition, RAG model, Natural Language Processing, Embeddings

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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