Realidad Aumentada Adaptativa

En la actualidad los avances presentados por empresas como Google, con sus �últimas propuestas tales como Google Glass, las cuales hacen uso de la Realidad Aumentada Adaptativa, son consideradas como tecnologí��as emergentes. La existencia de dispositivos ubicuos y m�óviles y la gran cantidad de sensores disponibles dotan al software con capacidades para percibir su entorno y adaptarse a �el y al usuario de la aplicaci�ón en tiempo real. La Realidad Aumentada Adaptativa puede servirse de estos mecanismos, por lo que el presente trabajo muestra el estado de la cuesti�ón en el �área de los sistemas con adaptabilidad al usuario, prestando una especial atenci�ón a la Adaptabilidad Web. El problema central que se aborda es dar respuesta a las siguientes preguntas: ¿Qu�é es la Realidad Aumentada Adaptativa? ¿C�ómo y cu�áles son los sistemas adaptables al usuario? ¿Qu�é caracterí��sticas del usuario son relevantes para la adaptaci�ón? ¿Qu�é modelos requiere la adaptabilidad? ¿Qu�é requieren los sistemas adaptativos web actuales para ajustarse a las necesidades del usuario? ¿Qu�é m�as se requiere para una realidad con adaptaci�ón inteligente? ¿Qu�é proyectos de investigaci�ón existen, cu�áles son sus arquitecturas y modelos? A la vista de las respuestas obtenidas, se proponen al �final del trabajo una serie de posibles lí��neas de investigaci�ón

Tourist Recommendation Systems: Solving Mobility in a Private Vehicle With Support for Parking

A tourist who visits a city in a very short time needs to visit the most important Points of Interest (POIs) they can be identified by relating them to the interests of the user, or they can choose the best POIs according to a specific requirement. Additionally, when the tourist arrives in a city or place given the distance from one POI to another, they could decide in real time to visit the city on foot or by private vehicle, among other alternatives; then, when they arrive at a certain POI, they need to identify parking spaces where they can leave the vehicle as long as there are parking spaces available and the cost of leaving the vehicle in that parking lot is known. To solve this problem, in this document, an application is presented, a tourist recommendation system that infers in real time the tourist’s interests by analyzing feelings about phrases inherent to images of tourist interest from the Facebook social network. With this information, the best POIs to visit are identified. In a complementary way, the tourist is shown the option to choose the POIs most visited by other tourists. To improve the proposal, the Tourist Recommendation System (TRS) offers the user two possibilities to generate the routes, they can choose to visit the chosen POIs on foot or by private vehicle; this change can be done in real time. Furthermore, when the tourist makes a stop during their visit, they can search for nearby parking lots, and from the chosen parking lot they can verify whether or not there is space available and the cost of said parking lot. With this contribution, tourists will have a TRS, which, in addition to identifying their interests, to choose the best POIs to visit from a list presented. And additionally, it solves the parking search problem, making the tourist reduce time when parking their vehicle

Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization Proposal

In recent years, recommender systems have been used as a solution to support tourists with recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD) – Orienteering Problem (OP) – Time Windows (TW), which analyzes in real time the user’s constraints and the points of interest’s constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm. Then, with a genetic algorithm (GA), we optimize the proposed itineraries to tourists for facilitating the organization of their visits. We also used a parametrized fitness function to include any element of the context to generate an optimized recommendation. Our recommender is different from others because it is scalable and adaptable to environmental changes and users’ interests, and it offers real-time recommendations. To test our recommender, we developed an application that uses our algorithm. Finally, 131 tourists used this recommender system and an analysis of users’ perceptions was developed. Metrics were also used to detect the percentage of precision, in order to determine the degree of accuracy of the recommender system. This study has implications for researchers interested in developing software to recommend the best itinerary for tourists with constraint controls with regard to the optimized itineraries

The models and their vocabulary for the Adaptive Augmented Reality A2R=Los modelos y su vocabulario para la Realidad Aumentada Adaptativa A2R

Adaptive Augmented Reality is an emerging technology that can support users in their daily life with useful information in real time activities. However, one of the problems identified is the lack of a formal definition of the models required for the development of a A2R system. Therefore our aim is to propose a detailed definition of the models needed for this type of systems. To achieve this goal we started with a review of user adaptive systems throughout history. Adaptive Web systems have their own proposals for models and features, as well as for adaptation mechanisms of both presentation and navigation. Nevertheless these results do not fully satisfy the needs of A2R systems, as the scope for adaptability in A2R systems is wider than in typical web systems. We present an initial proposal of the required models for A2R. Moreover, in the search for a formal ground in the definition these models, we explored state of the art ontologies, particularly ontologies related to user and environment modelling, two key aspects in A2R, and we analysed to what extent our models are covered

Propuesta de un Sistema de Recomendación Contextual para Rutas Turísticas basado en un Algoritmo Genético

Actualmente la gran cantidad de datos que deja el usuario en la web o mientras realiza sus actividades cotidianas obliga a los investigadores a abordar el desarrollo de tecnologías que permitan su adquisición y procesamiento sin interrumpir al usuario y solicitando autorización para capturar sus datos. Por el contrario, las multinacionales como Google, Microsoft, Amazon, entre otras, todo el tiempo están captando esos datos, muchas veces sin que el usuario sea consciente de ello. Por otro lado, el turismo es una actividad que para muchos países representa una importante fuente de ingresos. Además, los turistas hoy en día tienen muchas facilidades para moverse alrededor del mundo. Sin embargo, cada vez que el turista decide viajar, a pesar de toda la información que existe sobre el lugar que visitará, no siempre resulta fácil organizar los itinerarios de visita. Los sistemas de recomendación turística de rutas tienen como objetivo ayudar al turista a encontrar itinerarios turísticos que sean valiosos desde el punto de vista del disfrute del turista, en el sentido de que busque los mejores Puntos de Interés (POIs) y organice itinerarios considerando las restricciones del turista y el contexto de su visita. Esta tesis doctoral propone un novedoso sistema de recomendación de rutas. Para ello, se abordan dos problemas fundamentalmente: el modelado del usuario relacionado con el sistema de recomendación turística; y por otro lado, la obtención de las rutas mediante un proceso basado en agrupamiento y un algoritmo genético, el cual optimiza cada ruta considerando el tiempo disponible del turista, la ubicación de los POIs, así como sus intereses inferidos y/o declarados explícitamente por el propio turista, ya sea en forma de categorías de POIs o POIs concretos que desee visitar. Para resolver la inferencia de los intereses del usuario a partir de sus datos, se exploraron diferentes alternativas. Finalmente, se optó por una de ellas, analizar la huella dejada mientras se mueve y visita POIs en una o más ciudades. Para esto se usa el sistema GPS de su teléfono, y se propone un algoritmo para identificar los intereses del usuario. Finalmente, se evalúa la eficacia y la usabilidad del sistema propuesto con usuarios y se presentan las conclusiones y el trabajo futuro. ABSTRACT Currently, the large amount of data that the user leaves on the web or while carrying out their daily activities forces researchers to address the development of technologies that allow their acquisition and processing without interrupting the user and requesting authorization to capture their data. On the contrary, multinationals such as Google, Microsoft, Amazon, among others, are capturing this data all the time, often without the user being aware of it. On the other hand, tourism is an activity that for many countries represents an important source of income. In addition, tourists today have many facilities to move around the world. However, every time the tourist decides to travel, despite all the information that exists about the place to visit, it is not always easy to organize the visit itineraries. Tourist route recommendation systems aim to help tourists find tourist itineraries that are valuable from the point of view of tourist enjoyment, in the sense that they search for the best Points of Interest (POIs) and organize itineraries considering restrictions. of the tourist and the context of their visit. This doctoral thesis proposes a novel route recommendation system. To do this, two fundamental problems are addressed: user modeling related to the tourist recommendation system; and on the other hand, obtaining the routes through a process based on clustering and a genetic algorithm, which optimizes each route considering the tourist’s available time, the location of the POIs, as well as their inferred interests and/or or declared explicitly by the tourist himself, either in the form of categories of POIs or specific POIs that he wishes to visit. To solve the inference of the user’s interests from their data, different alternatives were explored. Finally, one of them was chosen, to analyze the footprint left while moving and visiting POIs in one or more cities. For this, the GPS system of your phone is used, and an algorithm is proposed to identify the interests of the user. Finally, the effectiveness and usability of the proposed system with users is evaluated and the conclusions and future work are presented