Recommender system for sport videos based on user audiovisual consumption

This paper describes a recommender system for sport videos, transmitted over the Internet and/or broadcast, in the context of large-scale events, which has been tested for the Olympic Games. The recommender is based on audiovisual consumption and does not depend on the number of users, running only on the client side. This avoids the concurrence, computation and privacy problems of central server approaches in scenarios with a large number of users, such as the Olympic Games. The system has been designed to take advantage of the information available in the videos, which is used along with the implicit information of the user and the modeling of his/her audiovisual content consumption. The system is thus transparent to the user, who does not need to take any specific action. Another important characteristic is that the system can produce recommendations for both live and recorded events. Testing has showed advantages compared to previous systems, as will be shown in the results.

Social and content hybrid image recommender system for mobile social networks

One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user.

Generating context-aware recommendations using banking data in a mobile recommender system

The increasing adoption of smartphones by the society has created a new area of research in recommender systems. This new domain is based on using location and context-awareness to provide personalization. This paper describes a model to generate context-aware recommendations for mobile recommender systems using banking data in order to recommend places where the bank customers have previously spent their money. In this work we have used real data provided by a well know Spanish bank. The mobile prototype deployed in the bank Labs environment was evaluated in a survey among 100 users with good results regarding usefulness and effectiveness. The results also showed that test users had a high confidence in a recommender system based on real banking data.

Incorporating reliability measurements into the predictions of a recommender system

In this paper we introduce the idea of using a reliability measure associated to the predic- tions made by recommender systems based on collaborative filtering. This reliability mea- sure is based on the usual notion that the more reliable a prediction, the less liable to be wrong. Here we will define a general reliability measure suitable for any arbitrary recom- mender system. We will also show a method for obtaining specific reliability measures specially fitting the needs of different specific recommender systems.

RESSIST: a research object-based recommender system

The scientific method is a methodological approach to the process of inquiry { in which empirically grounded theory of nature is constructed and verified [14]. It is a hard, exhaustive and dedicated multi-stage procedure that a researcher must perform to achieve valuable knowledge. Trying to help researchers during this process, a recommender system, intended as a researcher assistant, is designed to provide them useful tools and information for each stage of the procedure. A new similarity measure between research objects and a representational model, based on domain spaces, to handle them in dif ferent levels are created as well as a system to build them from OAI-PMH (and RSS) resources. It tries to represents a sound balance between scientific insight into individual scientific creative processes and technical implementation using innovative technologies in information extraction, document summarization and semantic analysis at a large scale.

A Model for Proactivity in Mobile, Context-aware Recommender Systems

A proactive recommender system pushes recommendations to the user when the current situation seems appropriate, without explicit user request. This is conceivable in mobile scenarios such as restaurant or gas station recommendations. In this paper, we present a model for proactivity in mobile recommender systems. The model relies on domain-dependent context modeling in several categories. The recommendation process is divided into two phases to first analyze the current situation and then examine the suitability of particular items. We have implemented a prototype gas station recommender and conducted a survey for evaluation. Results showed good correlation of the output of our system with the assessment of users regarding the question when to generate recommendations.

A collaborative filtering approach to mitigate the new user cold start problem.

The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommenda- tions received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system?s collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neu- ral learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave- one-out cross validation.

Improving Collaborative Filtering Based Recommender Systems Using Pareto Dominance

Los sistemas de recomendación son un tipo de solución al problema de sobrecarga de información que sufren los usuarios de los sitios web en los que se pueden votar ciertos artículos. El sistema de recomendación de filtrado colaborativo es considerado como el método con más éxito debido a que sus recomendaciones se hacen basándose en los votos de usuarios similares a un usuario activo. Sin embargo, el método de filtrado de colaboración tradicional selecciona usuarios insuficientemente representativos como vecinos de cada usuario activo. Esto significa que las recomendaciones hechas a posteriori no son lo suficientemente precisas. El método propuesto en esta tesis realiza un pre-filtrado del proceso, mediante el uso de dominancia de Pareto, que elimina los usuarios menos representativos del proceso de selección k-vecino y mantiene los más prometedores. Los resultados de los experimentos realizados en MovieLens y Netflix muestran una mejora significativa en todas las medidas de calidad estudiadas en la aplicación del método propuesto. ABSTRACTRecommender systems are a type of solution to the information overload problem suffered by users of websites on which they can rate certain items. The Collaborative Filtering Recommender System is considered to be the most successful approach as it make its recommendations based on votes of users similar to an active user. Nevertheless, the traditional collaborative filtering method selects insufficiently representative users as neighbors of each active user. This means that the recommendations made a posteriori are not precise enough. The method proposed in this thesis performs a pre-filtering process, by using Pareto dominance, which eliminates the less representative users from the k-neighbor selection process and keeps the most promising ones. The results from the experiments performed on Movielens and Netflix show a significant improvement in all the quality measures studied on applying the proposed method.

Incorporating Group Recommendations to Recommender Systems: Alternatives and Performance

La importancia de los sistemas de recomendación ha experimentado un crecimiento exponencial como consecuencia del auge de las redes sociales. En esta tesis doctoral presentaré una amplia visión sobre el estado del arte de los sistemas de recomendación. Incialmente, estos estaba basados en fitrado demográfico, basado en contendio o colaborativo. En la actualidad, estos sistemas incorporan alguna información social al proceso de recomendación. En el futuro utilizarán información implicita, local y personal proveniente del Internet de las cosas. Los sistemas de recomendación basados en filtrado colaborativo se pueden modificar con el fin de realizar recomendaciones a grupos de usuarios. Existen trabajos previos que han incluido estas modificaciones en diferentes etapas del algoritmo de filtrado colaborativo: búsqueda de los vecinos, predicción de las votaciones y elección de las recomendaciones. En esta tesis doctoral proporcionaré un nuevo método que realizar el proceso de unficación (pasar de varios usuarios a un grupo) en el primer paso del algoritmo de filtrado colaborativo: cálculo de la métrica de similaridad. Proporcionaré una formalización completa del método propuesto. Explicaré cómo obtener el conjunto de k vecinos del grupo de usuarios y mostraré cómo obtener recomendaciones usando dichos vecinos. Asimismo, incluiré un ejemplo detallando cada paso del método propuesto en un sistema de recomendación compuesto por 8 usuarios y 10 items. Las principales características del método propuesto son: (a) es más rápido (más eficiente) que las alternativas proporcionadas por otros autores, y (b) es al menos tan exacto y preciso como otras soluciones estudiadas. Para contrastar esta hipótesis realizaré varios experimentos que miden la precisión, la exactitud y el rendimiento del método. Los resultados obtenidos se compararán con los resultados de otras alternativas utilizadas en la recomendación de grupos. Los experimentos se realizarán con las bases de datos de MovieLens y Netflix. ABSTRACT The importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things. As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In this PhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation. I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present. The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct several experiments measuring the accuracy, the precision and the performance of my method. I will compare these results with the results generated by other methods of group recommendation. The experiments will be carried out using MovieLens and Netflix datasets.

Hierarchical graph maps for visualization of collaborative recommender systems

In this paper we provide a method that allows the visualization of similarity relationships present between items of collaborative filtering recommender systems, as well as the relative importance of each of these. The objective is to offer visual representations of the recommender system?s set of items and of their relationships; these graphs show us where the most representative information can be found and which items are rated in a more similar way by the recommender system?s community of users. The visual representations achieved take the shape of phylogenetic trees, displaying the numerical similarity and the reliability between each pair of items considered to be similar. As a case study we provide the results obtained using the public database Movielens 1M, which contains 3900 movies.

Energy and power aware scheduling algorithm modeling for battery powered embedded systems

Energy management has always been recognized as a challenge in mobile systems, especially in modern OS-based mobile systems where multi-functioning are widely supported. Nowadays, it is common for a mobile system user to run multiple applications simultaneously while having a target battery lifetime in mind for a specific application. Traditional OS-level power management (PM) policies make their best effort to save energy under performance constraint, but fail to guarantee a target lifetime, leaving the painful trading off between the total performance of applications and the target lifetime to the user itself. This thesis provides a new way to deal with the problem. It is advocated that a strong energy-aware PM scheme should first guarantee a user-specified battery lifetime to a target application by restricting the average power of those less important applications, and in addition to that, maximize the total performance of applications without harming the lifetime guarantee. As a support, energy, instead of CPU or transmission bandwidth, should be globally managed as the first-class resource by the OS. As the first-stage work of a complete PM scheme, this thesis presents the energy-based fair queuing scheduling, a novel class of energy-aware scheduling algorithms which, in combination with a mechanism of battery discharge rate restricting, systematically manage energy as the first-class resource with the objective of guaranteeing a user-specified battery lifetime for a target application in OS-based mobile systems. Energy-based fair queuing is a cross-application of the traditional fair queuing in the energy management domain. It assigns a power share to each task, and manages energy by proportionally serving energy to tasks according to their assigned power shares. The proportional energy use establishes proportional share of the system power among tasks, which guarantees a minimum power for each task and thus, avoids energy starvation on any task. Energy-based fair queuing treats all tasks equally as one type and supports periodical time-sensitive tasks by allocating each of them a share of system power that is adequate to meet the highest energy demand in all periods. However, an overly conservative power share is usually required to guarantee the meeting of all time constraints. To provide more effective and flexible support for various types of time-sensitive tasks in general purpose operating systems, an extra real-time friendly mechanism is introduced to combine priority-based scheduling into the energy-based fair queuing. Since a method is available to control the maximum time one time-sensitive task can run with priority, the power control and time-constraint meeting can be flexibly traded off. A SystemC-based test-bench is designed to assess the algorithms. Simulation results show the success of the energy-based fair queuing in achieving proportional energy use, time-constraint meeting, and a proper trading off between them. La gestión de energía en los sistema móviles está considerada hoy en día como un reto fundamental, notándose, especialmente, en aquellos terminales que utilizando un sistema operativo implementan múltiples funciones. Es común en los sistemas móviles actuales ejecutar simultaneamente diferentes aplicaciones y tener, para una de ellas, un objetivo de tiempo de uso de la batería. Tradicionalmente, las políticas de gestión de consumo de potencia de los sistemas operativos hacen lo que está en sus manos para ahorrar energía y satisfacer sus requisitos de prestaciones, pero no son capaces de proporcionar un objetivo de tiempo de utilización del sistema, dejando al usuario la difícil tarea de buscar un compromiso entre prestaciones y tiempo de utilización del sistema. Esta tesis, como contribución, proporciona una nueva manera de afrontar el problema. En ella se establece que un esquema de gestión de consumo de energía debería, en primer lugar, garantizar, para una aplicación dada, un tiempo mínimo de utilización de la batería que estuviera especificado por el usuario, restringiendo la potencia media consumida por las aplicaciones que se puedan considerar menos importantes y, en segundo lugar, maximizar las prestaciones globales sin comprometer la garantía de utilización de la batería. Como soporte de lo anterior, la energía, en lugar del tiempo de CPU o el ancho de banda, debería gestionarse globalmente por el sistema operativo como recurso de primera clase. Como primera fase en el desarrollo completo de un esquema de gestión de consumo, esta tesis presenta un algoritmo de planificación de encolado equitativo (fair queueing) basado en el consumo de energía, es decir, una nueva clase de algoritmos de planificación que, en combinación con mecanismos que restrinjan la tasa de descarga de una batería, gestionen de forma sistemática la energía como recurso de primera clase, con el objetivo de garantizar, para una aplicación dada, un tiempo de uso de la batería, definido por el usuario, en sistemas móviles empotrados. El encolado equitativo de energía es una extensión al dominio de la energía del encolado equitativo tradicional. Esta clase de algoritmos asigna una reserva de potencia a cada tarea y gestiona la energía sirviéndola de manera proporcional a su reserva. Este uso proporcional de la energía garantiza que cada tarea reciba una porción de potencia y evita que haya tareas que se vean privadas de recibir energía por otras con un comportamiento más ambicioso. Esta clase de algoritmos trata a todas las tareas por igual y puede planificar tareas periódicas en tiempo real asignando a cada una de ellas una reserva de potencia que es adecuada para proporcionar la mayor de las cantidades de energía demandadas por período. Sin embargo, es posible demostrar que sólo se consigue cumplir con los requisitos impuestos por todos los plazos temporales con reservas de potencia extremadamente conservadoras. En esta tesis, para proporcionar un soporte más flexible y eficiente para diferentes tipos de tareas de tiempo real junto con el resto de tareas, se combina un mecanismo de planificación basado en prioridades con el encolado equitativo basado en energía. En esta clase de algoritmos, gracias al método introducido, que controla el tiempo que se ejecuta con prioridad una tarea de tiempo real, se puede establecer un compromiso entre el cumplimiento de los requisitos de tiempo real y el consumo de potencia. Para evaluar los algoritmos, se ha diseñado en SystemC un banco de pruebas. Los resultados muestran que el algoritmo de encolado equitativo basado en el consumo de energía consigue el balance entre el uso proporcional a la energía reservada y el cumplimiento de los requisitos de tiempo real.

A Balanced Memory-Based Collaborative Filtering Similarity Measure.

Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures.

Gestión óptima de embalses en avenidas incorporando al concepto de incertidumbre : aplicación a embalses con central hidroeléctrica

Esta tesis realiza una contribución metodológica al problema de la gestión óptima de embalses hidroeléctricos durante eventos de avenidas, considerando un enfoque estocástico y multiobjetivo. Para ello se propone una metodología de evaluación de estrategias de laminación en un contexto probabilístico y multiobjetivo. Además se desarrolla un entorno dinámico de laminación en tiempo real con pronósticos que combina un modelo de optimización y algoritmos de simulación. Estas herramientas asisten a los gestores de las presas en la toma de decisión respecto de cuál es la operación más adecuada del embalse. Luego de una detallada revisión de la bibliografía, se observó que los trabajos en el ámbito de la gestión óptima de embalses en avenidas utilizan, en general, un número reducido de series de caudales o hidrogramas para caracterizar los posibles escenarios. Limitando el funcionamiento satisfactorio de un modelo determinado a situaciones hidrológicas similares. Por otra parte, la mayoría de estudios disponibles en este ámbito abordan el problema de la laminación en embalses multipropósito durante la temporada de avenidas, con varios meses de duración. Estas características difieren de la realidad de la gestión de embalses en España. Con los avances computacionales en materia de gestión de información en tiempo real, se observó una tendencia a la implementación de herramientas de operación en tiempo real con pronósticos para determinar la operación a corto plazo (involucrando el control de avenidas). La metodología de evaluación de estrategias propuesta en esta tesis se basa en determinar el comportamiento de éstas frente a un espectro de avenidas características de la solicitación hidrológica. Con ese fin, se combina un sistema de evaluación mediante indicadores y un entorno de generación estocástica de avenidas, obteniéndose un sistema implícitamente estocástico. El sistema de evaluación consta de tres etapas: caracterización, síntesis y comparación, a fin de poder manejar la compleja estructura de datos resultante y realizar la evaluación. En la primera etapa se definen variables de caracterización, vinculadas a los aspectos que se quieren evaluar (seguridad de la presa, control de inundaciones, generación de energía, etc.). Estas variables caracterizan el comportamiento del modelo para un aspecto y evento determinado. En la segunda etapa, la información de estas variables se sintetiza en un conjunto de indicadores, lo más reducido posible. Finalmente, la comparación se lleva a cabo a partir de la comparación de esos indicadores, bien sea mediante la agregación de dichos objetivos en un indicador único, o bien mediante la aplicación del criterio de dominancia de Pareto obteniéndose un conjunto de soluciones aptas. Esta metodología se aplicó para calibrar los parámetros de un modelo de optimización de embalse en laminación y su comparación con otra regla de operación, mediante el enfoque por agregación. Luego se amplió la metodología para evaluar y comparar reglas de operación existentes para el control de avenidas en embalses hidroeléctricos, utilizando el criterio de dominancia. La versatilidad de la metodología permite otras aplicaciones, tales como la determinación de niveles o volúmenes de seguridad, o la selección de las dimensiones del aliviadero entre varias alternativas. Por su parte, el entorno dinámico de laminación al presentar un enfoque combinado de optimización-simulación, permite aprovechar las ventajas de ambos tipos de modelos, facilitando la interacción con los operadores de las presas. Se mejoran los resultados respecto de los obtenidos con una regla de operación reactiva, aun cuando los pronósticos se desvían considerablemente del hidrograma real. Esto contribuye a reducir la tan mencionada brecha entre el desarrollo teórico y la aplicación práctica asociada a los modelos de gestión óptima de embalses. This thesis presents a methodological contribution to address the problem about how to operate a hydropower reservoir during floods in order to achieve an optimal management considering a multiobjective and stochastic approach. A methodology is proposed to assess the flood control strategies in a multiobjective and probabilistic framework. Additionally, a dynamic flood control environ was developed for real-time operation, including forecasts. This dynamic platform combines simulation and optimization models. These tools may assist to dam managers in the decision making process, regarding the most appropriate reservoir operation to be implemented. After a detailed review of the bibliography, it was observed that most of the existing studies in the sphere of flood control reservoir operation consider a reduce number of hydrographs to characterize the reservoir inflows. Consequently, the adequate functioning of a certain strategy may be limited to similar hydrologic scenarios. In the other hand, most of the works in this context tackle the problem of multipurpose flood control operation considering the entire flood season, lasting some months. These considerations differ from the real necessity in the Spanish context. The implementation of real-time reservoir operation is gaining popularity due to computational advances and improvements in real-time data management. The methodology proposed in this thesis for assessing the strategies is based on determining their behavior for a wide range of floods, which are representative of the hydrological forcing of the dam. An evaluation algorithm is combined with a stochastic flood generation system to obtain an implicit stochastic analysis framework. The evaluation system consists in three stages: characterizing, synthesizing and comparing, in order to handle the complex structure of results and, finally, conduct the evaluation process. In the first stage some characterization variables are defined. These variables should be related to the different aspects to be evaluated (such as dam safety, flood protection, hydropower, etc.). Each of these variables characterizes the behavior of a certain operating strategy for a given aspect and event. In the second stage this information is synthesized obtaining a reduced group of indicators or objective functions. Finally, the indicators are compared by means of an aggregated approach or by a dominance criterion approach. In the first case, a single optimum solution may be achieved. However in the second case, a set of good solutions is obtained. This methodology was applied for calibrating the parameters of a flood control model and to compare it with other operating policy, using an aggregated method. After that, the methodology was extent to assess and compared some existing hydropower reservoir flood control operation, considering the Pareto approach. The versatility of the method allows many other applications, such as determining the safety levels, defining the spillways characteristics, among others. The dynamic framework for flood control combines optimization and simulation models, exploiting the advantages of both techniques. This facilitates the interaction between dam operators and the model. Improvements are obtained applying this system when compared with a reactive operating policy, even if the forecasts deviate significantly from the observed hydrograph. This approach contributes to reduce the gap between the theoretical development in the field of reservoir management and its practical applications.

MOIRA-PLUS use in decision making on the long-term management of contaminated freshwater bodies and catchments

In the mid-long-term after a nuclear accident, the contamination of drinking water sources, fish and other aquatic foodstuffs, irrigation supplies and people?s exposure during recreational activities may create considerable public concern, even though dose assessment may in certain situations indicate lesser importance than for other sources, as clearly experienced in the aftermath of past accidents. In such circumstances there are a number of available countermeasure options, ranging from specific chemical treatment of lakes to bans on fish ingestion or on the use of water for crop irrigation. The potential actions can be broadly grouped into four main categories, chemical, biological, physical and social. In some cases a combination of actions may be the optimal strategy and a decision support system (DSS) like MOIRA-PLUS can be of great help to optimise a decision. A further option is of course not to take any remedial actions, although this may also have significant socio-economic repercussions which should be adequately evaluated. MOIRA-PLUS is designed to allow for a reliable assessment of the long-term evolution of the radiological situation and of feasible alternative rehabilitation strategies, including an objective evaluation of their social, economic and ecological impacts in a rational and comprehensive manner. MOIRA-PLUS also features a decision analysis methodology, making use of multi-attribute analysis, which can take into account the preferences and needs of different types of stakeholders. The main functions and elements of the system are described summarily. Also the conclusions from end-user?s experiences with the system are discussed, including exercises involving the organizations responsible for emergency management and the affected services, as well as different local and regional stakeholders. MOIRAPLUS has proven to be a mature system, user friendly and relatively easy to set up. It can help to better decisionmaking by enabling a realistic evaluation of the complete impacts of possible recovery strategies. Also, the interaction with stakeholders has allowed identifying improvements of the system that have been recently implemented.

Optimizing PV use through active demand side management

With recent technological developments within the field of power conditioning and the progressive decrease of incentives for PV electricity in grid-connected markets, new operation modes for PV systems should be explored beyond the traditional maximization of PV electri city feed-in. An example can be found in the domestic sector, where the use of modern PV hybrid systems combin ed with efficient electrical appliances and demand side management strategies can significantly enhance the PV value for the user. This paper presents an active demand side management system able to displace the consumer’s load curve in response to local (PV hybrid system, user) and external conditions (external grid). In this way, th e consumer becomes an “active consumer” that can also cooperate with others and the grid, increasing even more the PV value for the electrical system.