Evolutionary Approach for Building, Exploring and Recommending Complex Items With Application in Nutritional Interventions

Over the last few years, the ability of recommender systems to help us in different environments has been increasing. Several systems try to offer solutions in highly complex environments such as nutrition, housing, or traveling. In this paper, we present a recommendation system capable of using different input sources (data and knowledge-based) and producing a complex structured output. We have used an evolutionary approach to combine several unitary items within a flexible structure and have built an initial set of complex configurable items. Then, a content-based approach refines (in terms of preferences) these candidates to offer a final recommendation.We conclude with the application of this approach to the healthy diet recommendation problem, addressing its strengths in this domain.Over the last few years, the ability of recommender systems to help us in different environments has been increasing. Several systems try to offer solutions in highly complex environments such as nutrition, housing, or traveling. In this paper, we present a recommendation system capable of using different input sources (data and knowledge-based) and producing a complex structured output. We have used an evolutionary approach to combine several unitary items within a flexible structure and have built an initial set of complex configurable items. Then, a content-based approach refines (in terms of preferences) these candidates to offer a final recommendation.We conclude with the application of this approach to the healthy diet recommendation problem, addressing its strengths in this domainEuropean Union (Stance4Health) under Grant 816303Ministerio de Ciencia e Innovación under Grant PID2021-123960OB-I00MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia estatal de Investigacion)/10.13039/501100011033ERDF (European Regional Development Fund)A way of making Europe. And in part under Grant TED2021-129402B-C21 funded by MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia estatal de Investigacion)/10.13039/501100011033European Union NextGenerationEU/PRTR (Plan de Recuperación, Transformación y Resiliencia)‘Program of Information and Communication technologies’’ at the University of Granad

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 816303 and from the Plan Propio de Investigación y Transferencia of the University of Granada under the program “Intensificación de la Investigación, modalidad B”.Acknowledgments: This work is part of the doctoral thesis of Daniel Hinojosa-Nogueira conducted within the context of the “Program of Nutrition and Food Sciences” at the University of Granada and part of the doctoral thesis of Bartolome Ortiz-Viso conducted within the context of the “Program of Information and Communication technologies” at the University of Granada.Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user’s needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.European Union’s Horizon 2020 research and innovation programme under grant agreement No 816303Plan Propio de Investigación y Transferencia of the University of Granada under the program “Intensificación de la Investigación, modalidad B

Development of an Unified Food Composition Database for the European Project “Stance4Health”

The European Commission funded project Stance4Health (S4H) aims to develop a complete personalised nutrition service. In order to succeed, sources of information on nutritional composition and other characteristics of foods need to be as comprehensive as possible. Food composition tables or databases (FCT/FCDB) are the most commonly used tools for this purpose. The aim of this study is to describe the harmonisation efforts carried out to obtain the Stance4Health FCDB. A total of 10 FCT/FCDB were selected from different countries and organizations. Data were classified using FoodEx2 and INFOODS tagnames to harmonise the information. Hazard analysis and critical control points analysis was applied as the quality control method. Data were processed by spreadsheets and MySQL. S4H’s FCDB is composed of 880 elements, including nutrients and bioactive compounds. A total of 2648 unified foods were used to complete the missing values of the national FCDB used. Recipes and dishes were estimated following EuroFIR standards via linked tables. S4H’s FCDB will be part of the smartphone app developed in the framework of the Stance4Health European project, which will be used in different personalized nutrition intervention studies. S4H FCDB has great perspectives, being one of the most complete in terms of number of harmonized foods, nutrients and bioactive compounds included.European Research Commission (Research Executive Agency) under the research project Stance4Health (Grant Contract No. 816303)“Plan propio de Investigación y Transferencia” of the University of Granada under the programs “Intensificación de la Investigación, modalidad B

User-dependent Flexible Recommender Systems

Recommender systems are ubiquitous in today’s technological landscape, integral to various services. Their prevalence is driven by the massive flow of information on the internet, and without effective filtering, user experiences would suffer. As powerful tools, recommender systems facilitate optimal interaction with the digital world by extracting relevant information tailored to individual needs. Despite their widespread use, recommender systems are also commercial tools, prompting extensive research in recommendation techniques and algorithms. This fast-paced research area holds the potential for significant impacts on our lives, raising concerns about their influence on both physical and psychological aspects. In the academic domain, recommender systems serve diverse applications, with a primary focus on assisting users in media service selection or product choices in e-commerce. The evolution of these systems, fueled by advances like neural networks, expanded their applicability beyond traditional domains to areas such as news and e-learning. Despite advancements, challenges persist in effectively applying recommender systems, particularly in complex scenarios influenced by contextual factors and diverse objectives. The need for personalized recommendations, considering multiple items, their order, and additional contextual factors like health considerations and expert opinions, presents an intriguing problem lacking a clear resolution strategy. This thesis aims to address these complex recommendation scenarios by developing new methodologies and software tools. The combination of various elements, textual features, expert sources, and user preferences forms the forefront of this recommendation problem, requiring innovative solutions. With this purpose, we propose an initial approach to recommendation systems in complex environments, highlighting the differences between them and their potential applications in a novel classification. In this classification, we emphasize the source of complexity in various scenarios by collecting studies that have explored these environments. Once this classification is completed, we proceed to present our approach to the problem. In our case, it involves a dual approach: firstly, we use those strong constraints to obtain combinations of items that can be recommendations. These solutions are created through a genetic algorithm that evaluates different designated constraints to find multiple composite solutions. Furthermore, the stochastic nature of the system allows us not only to satisfy numerous constraints but also to have great diversity and adaptability. Once these initial solutions are obtained, in the second phase of our system, we refine our recommendation. This second phase typically focuses on user preferences, which are less restrictively profound than the previous ones and allow for greater variability. This approach is tested in the domains of nutrition and podcasts. The former is an application within the European project Stance4Health, utilizing the initial prototype of our system. In the podcast application, we offer a less strict type of recommendation than the first, allowing for greater enhancement in line with user preferences. The second system is configured as a Python package, enabling broader replicability and the use of our approach in various scenarios. Subsequently, our approach delves into understanding user interests from a psychological perspective in recommendations and identifies what is necessary for users to follow recommendations. This is particularly relevant in the case of health-based nutrition recommendations, as users may not notice an immediate beneficial effect from the system, but the long-term effects are positive. To that end, we conduct a theoretical study on parameters that increase user engagement and subsequently evaluate it in the health application Stance4Health. From the research done in this field, we find that justifications, by enhancing the explainability of the system, can make users perceive our recommendations as more useful and interesting. However, obtaining these justifications can be costly. To address this, we propose a supervised algorithm that filters documents from nutrition experts to build an evidence-based database, enabling nutritional justifications based on the evaluation of our recipe. Finally, we provide an estimate of the greenhouse gas emissions generated by our approach throughout the thesis, with data allowing estimation per individual execution.Los sistemas de recomendación son omnipresentes en el panorama tecnológico actual, apareciendo en la mayoria de aplicaciones que usamos hoy en dia. Su prevalencia se debe a las enormes cantidades de información que se vierten a internet dia a dia y a como, sin un filtrado efectivo, la experiencia del usuario sería totalmente inoperativa. Los sistemas de recomendación facilitan la interacción óptima con el mundo digital al extraer información relevante de los elementos con los cuales interactuamos y facilitandonos el acceso a aquellos que es mas probable que nos interesen segun las necesidades individuales. A pesar de su uso generalizado, los sistemas de recomendación también son herramientas comerciales, lo que ha impulsado una amplia investigación en técnicas y algoritmos de recomendación. Esta área de investigación tiene el potencial de tener impactos significativos en nuestras vidas, generando preocupaciones sobre su influencia tanto en aspectos físicos como psicológicos. En el ámbito académico, los sistemas de recomendación tienen diversas aplicaciones, centrándose principalmente en ayudar a los usuarios en la selección de servicios o productos en comercio electrónico. La evolución de estos sistemas, impulsada por avances como las redes neuronales, amplió su aplicabilidad más allá de los dominios tradicionales a áreas como noticias y canciones o películas. A pesar de estos avances, aun hay desafíos en la aplicación efectiva de los sistemas de recomendación, especialmente en escenarios complejos influenciados por factores contextuales y objetivos diversos. La necesidad de recomendaciones personalizadas, considerando múltiples elementos, su orden y factores contextuales adicionales como consideraciones de salud y opiniones de expertos, plantea un problema intrigante que carece de una estrategia clara de resolución. Esta tesis tiene como objetivo abordar estos escenario de recomendacion denominados complejos mediante el desarrollo de nuevas metodlogias y herramientas de software. La combinación de varios elementos, características textuales, fuentes de expertos y preferencias del usuario constituye la vanguardia de este problema de recomendación, requiriendo soluciones innovadoras. Con este propósito, proponemos un enfoque inicial para sistemas de recomendación en entornos complejos, destacando las diferencias entre ellos y sus posibles aplicaciones en una clasificación novedosa. En esta clasificación, resaltamos la fuente de complejidad en diversos escenarios mediante la recopilación de estudios que han explorado estos entornos. Una vez completada esta clasificación, procedemos a presentar nuestro enfoque para el problema. En nuestro caso, implica un enfoque dual:primeramente utilizamos aquellas restricciones fuertes para obtener combinaciones de items que puedan ser recomendaciones. Estas soluciones se crean mediante un algoritmo genetico que evalua las diferentes restricciones designadas con el fin de encontrar multiples soluciones compuestas. Ademas, la estocasticidad del sistema nos permite no solo satisfacer numerosas restricciones, si no tener una gran diversidad y adaptabilidad. Una vez obtenidas estas soluciones iniciales, en la segunda fase de nuestro sistema, refinamos nuestra recomendación. Esta segunda fase se centra habitualmente en las prefrencias del usuario, que son de un menor calado restrictivo que las anteriores y admiten una mayor variabilidad . Este enfoque se prueba en los ámbitos de nutrición y podcasts. El primero es una aplicación dentro del proyecto europeo Stance4Health, utilizando el prototipo inicial de nuestro sistema. En la aplicación de podcasts, ofrecemos un tipo de recomendación menos estricta que la primera, permitiendo una mayor mejora en línea con las preferencias del usuario. El segundo sistema se configura a partir de un paquete de Python, permitiendo una replicabilidad más amplia y el uso de nuestro enfoque en diversos escenarios. Posteriormente, nuestro enfoque profundiza en comprender los intereses del usuario desde una perspectiva psicológica en las recomendaciones e identifica lo necesario para que los usuarios sigan las recomendaciones. Esto es particularmente relevante en el caso de las recomendaciones de nutrición basadas en la salud, ya que el usuario puede no notar un efecto beneficioso inmedito por el sistema, pero a la larga los efectos son positivos. Con ese fin, realizamos un estudio teórico sobre parámetros que aumentan la participación del usuario y posteriormente lo evaluamos en la aplicación de salud Stance4Health. A partir de la literatura, encontramos que las justificaciones, al mejorar la explicabilidad del sistema, pueden hacer que los usuarios perciban nuestras recomendaciones como más útiles e interesantes. Sin embargo, obtener estas justificaciones puede ser costoso. Para abordar esto, proponemos un algoritmo supervisado que filtra documentos de expertos en nutrición para construir una base de datos basada en evidencia, permitiendo justificaciones nutricionales basadas en la evaluación de nuestra receta. Finalmente, proporcionamos una estimación de las emisiones de gases de efecto invernadero generadas por nuestro enfoque a lo largo de toda la tesis, con datos que permiten la estimación por ejecución individual.Tesis Univ. Granada.10.13039/100010661-European Union (Stance4Health) (Grant Number: 816303)10.13039/501100004837-Ministerio de Ciencia e Innovación funded by MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia estatal de Investigacion)/10.13039/ 501100011033 and by ERDF (European Regional Development Fund) A way of making Europe (Grant Number: PID2021-123960OB-I00)10.13039/501100004837-MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia estatal de Investigacion)/10.13039/50110001103