Towards smart user models in open environments

Our work is concerned with user modelling in open environments. Our proposal then is the line of contributions to the advances on user modelling in open environments thanks so the Agent Technology, in what has been called Smart User Model. Our research contains a holistic study of User Modelling in several research areas related to users. We have developed a conceptualization of User Modelling by means of examples from a broad range of research areas with the aim of improving our understanding of user modelling and its role in the next generation of open and distributed service environments. This report is organized as follow: In chapter 1 we introduce our motivation and objectives. Then in chapters 2, 3, 4 and 5 we provide the state-of-the-art on user modelling. In chapter 2, we give the main definitions of elements described in the report. In chapter 3, we present an historical perspective on user models. In chapter 4 we provide a review of user models from the perspective of different research areas, with special emphasis on the give-and-take relationship between Agent Technology and user modelling. In chapter 5, we describe the main challenges that, from our point of view, need to be tackled by researchers wanting to contribute to advances in user modelling. From the study of the state-of-the-art follows an exploratory work in chapter 6. We define a SUM and a methodology to deal with it. We also present some cases study in order to illustrate the methodology. Finally, we present the thesis proposal to continue the work, together with its corresponding work scheduling and temporalisation

Methodology to obtain the user's Human Values Scale from Smart User Models

En años recientes,la Inteligencia Artificial ha contribuido a resolver problemas encontrados en el desempeño de las tareas de unidades informáticas, tanto si las computadoras están distribuidas para interactuar entre ellas o en cualquier entorno (Inteligencia Artificial Distribuida). Las Tecnologías de la Información permiten la creación de soluciones novedosas para problemas específicos mediante la aplicación de los hallazgos en diversas áreas de investigación. Nuestro trabajo está dirigido a la creación de modelos de usuario mediante un enfoque multidisciplinario en los cuales se emplean los principios de la psicología, inteligencia artificial distribuida, y el aprendizaje automático para crear modelos de usuario en entornos abiertos; uno de estos es la Inteligencia Ambiental basada en Modelos de Usuario con funciones de aprendizaje incremental y distribuido (conocidos como Smart User Model). Basándonos en estos modelos de usuario, dirigimos esta investigación a la adquisición de características del usuario importantes y que determinan la escala de valores dominantes de este en aquellos temas en los cuales está más interesado, desarrollando una metodología para obtener la Escala de Valores Humanos del usuario con respecto a sus características objetivas, subjetivas y emocionales (particularmente en Sistemas de Recomendación).Una de las áreas que ha sido poco investigada es la inclusión de la escala de valores humanos en los sistemas de información. Un Sistema de Recomendación, Modelo de usuario o Sistemas de Información, solo toman en cuenta las preferencias y emociones del usuario [Velásquez, 1996, 1997; Goldspink, 2000; Conte and Paolucci, 2001; Urban and Schmidt, 2001; Dal Forno and Merlone, 2001, 2002; Berkovsky et al., 2007c]. Por lo tanto, el principal enfoque de nuestra investigación está basado en la creación de una metodología que permita la generación de una escala de valores humanos para el usuario desde el modelo de usuario. Presentamos resultados obtenidos de un estudio de casos utilizando las características objetivas, subjetivas y emocionales en las áreas de servicios bancarios y de restaurantes donde la metodología propuesta en esta investigación fue puesta a prueba.En esta tesis, las principales contribuciones son: El desarrollo de una metodología que, dado un modelo de usuario con atributos objetivos, subjetivos y emocionales, se obtenga la Escala de Valores Humanos del usuario. La metodología propuesta está basada en el uso de aplicaciones ya existentes, donde todas las conexiones entre usuarios, agentes y dominios que se caracterizan por estas particularidades y atributos; por lo tanto, no se requiere de un esfuerzo extra por parte del usuario.

User modeling In adaptive hypermedia educational systems

This document is a survey in the research area of User Modeling (UM) for the specific field of Adaptive Learning. The aims of this document are: To define what it is a User Model; To present existing and well known User Models; To analyze the existent standards related with UM; To compare existing systems. In the scientific area of User Modeling (UM), numerous research and developed systems already seem to promise good results, but some experimentation and implementation are still necessary to conclude about the utility of the UM. That is, the experimentation and implementation of these systems are still very scarce to determine the utility of some of the referred applications. At present, the Student Modeling research goes in the direction to make possible reuse a student model in different systems. The standards are more and more relevant for this effect, allowing systems communicate and to share data, components and structures, at syntax and semantic level, even if most of them still only allow syntax integration.

Enabling a direct path from end-user specifications to executable protocols in a biology laboratory environment

Biomedical analyses are becoming increasingly complex, with respect to both the type of the data to be produced and the procedures to be executed. This trend is expected to continue in the future. The development of information and protocol management systems that can sustain this challenge is therefore becoming an essential enabling factor for all actors in the field. The use of custom-built solutions that require the biology domain expert to acquire or procure software engineering expertise in the development of the laboratory infrastructure is not fully satisfactory because it incurs undesirable mutual knowledge dependencies between the two camps. We propose instead an infrastructure concept that enables the domain experts to express laboratory protocols using proper domain knowledge, free from the incidence and mediation of the software implementation artefacts. In the system that we propose this is made possible by basing the modelling language on an authoritative domain specific ontology and then using modern model-driven architecture technology to transform the user models in software artefacts ready for execution in a multi-agent based execution platform specialized for biomedical laboratories.

AALUMO: a user model ontology for ambient assisted living services supported in next-generation networks

Ambient Assisted Living (AAL) services are emerging as context-awareness solutions to support elderly people?s autonomy. The context-aware paradigm makes applications more user-adaptive. In this way, context and user models expressed in ontologies are employed by applications to describe user and environment characteristics. The rapid advance of technology allows creating context server to relieve applications of context reasoning techniques. Specifically, the Next Generation Networks (NGN) provides by means of the presence service a framework to manage the current user's state as well as the user's profile information extracted from Internet and mobile context. This paper propose a user modeling ontology for AAL services which can be deployed in a NGN environment with the aim at adapting their functionalities to the elderly's context information and state.

A reflective characterization of occasional user

This work revisits established user classifications and aims to characterise a historically unspecified user category, the Occasional User (OU). Three user categories, novice, intermediate and expert, have dominated the work of user interface (UI) designers, researchers and educators for decades. These categories were created to conceptualise user's needs, strategies and goals around the 80s. Since then, UI paradigm shifts, such as direct manipulation and touch, along with other advances in technology, gave new access to people with little computer knowledge. This fact produced a diversification of the existing user categories not observed in the literature review of traditional classification of users. The findings of this work include a new characterisation of the occasional user, distinguished by user's uncertainty of repetitive use of an interface and little knowledge about its functioning. In addition, the specification of the OU, together with principles and recommendations will help UI community to informatively design for users without requiring a prospective use and previous knowledge of the UI. The OU is an essential type of user to apply user-centred design approach to understand the interaction with technology as universal, accessible and transparent for the user, independently of accumulated experience and technological era that users live in.

Enriching e-learning metadata through digital library usage analysis

Peer-reviewed

Collaborative recommender agents based on case-based reasoning and trust

La comunitat científica que treballa en Intel·ligència Artificial (IA) ha dut a terme una gran quantitat de treball en com la IA pot ajudar a les persones a trobar el que volen dins d'Internet. La idea dels sistemes recomanadors ha estat extensament acceptada pels usuaris. La tasca principal d'un sistema recomanador és localitzar ítems, fonts d'informació i persones relacionades amb els interessos i preferències d'una persona o d'un grup de persones. Això comporta la construcció de models d'usuari i l'habilitat d'anticipar i predir les preferències de l'usuari. Aquesta tesi està focalitzada en l'estudi de tècniques d'IA que millorin el rendiment dels sistemes recomanadors. Inicialment, s'ha dut a terme un anàlisis detallat de l'actual estat de l'art en aquest camp. Aquest treball ha estat organitzat en forma de taxonomia on els sistemes recomanadors existents a Internet es classifiquen en 8 dimensions generals. Aquesta taxonomia ens aporta una base de coneixement indispensable pel disseny de la nostra proposta. El raonament basat en casos (CBR) és un paradigma per aprendre i raonar a partir de la experiència adequat per sistemes recomanadors degut als seus fonaments en el raonament humà. Aquesta tesi planteja una nova proposta de CBR aplicat al camp de la recomanació i un mecanisme d'oblit per perfils basats en casos que controla la rellevància i edat de les experiències passades. Els resultats experimentals demostren que aquesta proposta adapta millor els perfils als usuaris i soluciona el problema de la utilitat que pateixen el sistemes basats en CBR. Els sistemes recomanadors milloren espectacularment la qualitat dels resultats quan informació sobre els altres usuaris és utilitzada quan es recomana a un usuari concret. Aquesta tesi proposa l'agentificació dels sistemes recomanadors per tal de treure profit de propietats interessants dels agents com ara la proactivitat, la encapsulació o l'habilitat social. La col·laboració entre agents es realitza a partir del mètode de filtratge basat en la opinió i del mètode col·laboratiu de filtratge a partir de confiança. Els dos mètodes es basen en un model social de confiança que fa que els agents siguin menys vulnerables als altres quan col·laboren. Els resultats experimentals demostren que els agents recomanadors col·laboratius proposats milloren el rendiment del sistema mentre que preserven la privacitat de les dades personals de l'usuari. Finalment, aquesta tesi també proposa un procediment per avaluar sistemes recomanadors que permet la discussió científica dels resultats. Aquesta proposta simula el comportament dels usuaris al llarg del temps basat en perfils d'usuari reals. Esperem que aquesta metodologia d'avaluació contribueixi al progrés d'aquesta àrea de recerca.

Mediating between hearer's and speaker's views in the generation of adaptive explanations

In this article, we examine the case of a system that cooperates with a “direct” user to plan an activity that some “indirect” user, not interacting with the system, should perform. The specific application we consider is the prescription of drugs. In this case, the direct user is the prescriber and the indirect user is the person who is responsible for performing the therapy. Relevant characteristics of the two users are represented in two user models. Explanation strategies are represented in planning operators whose preconditions encode the cognitive state of the indirect user; this allows tailoring the message to the indirect user's characteristics. Expansion of optional subgoals and selection among candidate operators is made by applying decision criteria represented as metarules, that negotiate between direct and indirect users' views also taking into account the context where explanation is provided. After the message has been generated, the direct user may ask to add or remove some items, or change the message style. The system defends the indirect user's needs as far as possible by mentioning the rationale behind the generated message. If needed, the plan is repaired and the direct user model is revised accordingly, so that the system learns progressively to generate messages suited to the preferences of people with whom it interacts.

Simple prompts reduce inadvertent energy consumption from lighting in office buildings

Building designs regularly fail to achieve the anticipated levels of in-use energy consumption. The interaction of occupants with building controls is often cited as a key factor behind this discrepancy. This paper examines whether one factor in inadvertent energy consumption might be the appearance of post-completion errors (when an intended action is not taken because a primary goal has already been accomplished) in occupants’ interactions with building controls. Post-completion errors have been widely studied in human-computer interaction but the concept has not previously been applied to the interaction of occupants with building controls. Two experiments were carried out to examine the effect of incorporating two different types of simple prompt to reduce post-completion error in the use of light switches in office meeting rooms. Results showed that the prompts were effective and that occupants switched off lights when leaving the room more often when presented with a normative prompt than with a standard injunction. Additionally, an over reliance on PIR sensors to turn off lights after meetings was observed, which reduced their intended energy savings. We conclude that achieving low carbon buildings in practice is not solely a technological issue and that application of user-models from human-computer interaction will encourage appropriate occupant interaction with building controls and help reduce inadvertent energy consumption.

Arquitectura y modelo de referencia para un ecosistema de intercambio de datos personales centrado en el usuario

Los servicios telemáticos han transformando la mayoría de nuestras actividades cotidianas y ofrecen oportunidades sin precedentes con características como, por ejemplo, el acceso ubicuo, la disponibilidad permanente, la independencia del dispositivo utilizado, la multimodalidad o la gratuidad, entre otros. No obstante, los beneficios que destacan en cuanto se reflexiona sobre estos servicios, tienen como contrapartida una serie de riesgos y amenazas no tan obvios, ya que éstos se nutren de y tratan con datos personales, lo cual suscita dudas respecto a la privacidad de las personas. Actualmente, las personas que asumen el rol de usuarios de servicios telemáticos generan constantemente datos digitales en distintos proveedores. Estos datos reflejan parte de su intimidad, de sus características particulares, preferencias, intereses, relaciones sociales, hábitos de consumo, etc. y lo que es más controvertido, toda esta información se encuentra bajo la custodia de distintos proveedores que pueden utilizarla más allá de las necesidades y el control del usuario. Los datos personales y, en particular, el conocimiento sobre los usuarios que se puede extraer a partir de éstos (modelos de usuario) se han convertido en un nuevo activo económico para los proveedores de servicios. De este modo, estos recursos se pueden utilizar para ofrecer servicios centrados en el usuario basados, por ejemplo, en la recomendación de contenidos, la personalización de productos o la predicción de su comportamiento, lo cual permite a los proveedores conectar con los usuarios, mantenerlos, involucrarlos y en definitiva, fidelizarlos para garantizar el éxito de un modelo de negocio. Sin embargo, dichos recursos también pueden utilizarse para establecer otros modelos de negocio que van más allá de su procesamiento y aplicación individual por parte de un proveedor y que se basan en su comercialización y compartición con otras entidades. Bajo esta perspectiva, los usuarios sufren una falta de control sobre los datos que les refieren, ya que esto depende de la voluntad y las condiciones impuestas por los proveedores de servicios, lo cual implica que habitualmente deban enfrentarse ante la disyuntiva de ceder sus datos personales o no acceder a los servicios telemáticos ofrecidos. Desde el sector público se trata de tomar medidas que protejan a los usuarios con iniciativas y legislaciones que velen por su privacidad y que aumenten el control sobre sus datos personales, a la vez que debe favorecer el desarrollo económico propiciado por estos proveedores de servicios. En este contexto, esta tesis doctoral propone una arquitectura y modelo de referencia para un ecosistema de intercambio de datos personales centrado en el usuario que promueve la creación, compartición y utilización de datos personales y modelos de usuario entre distintos proveedores, al mismo tiempo que ofrece a los usuarios las herramientas necesarias para ejercer su control en cuanto a la cesión y uso de sus recursos personales y obtener, en su caso, distintos incentivos o contraprestaciones económicas. Las contribuciones originales de la tesis son la especificación y diseño de una arquitectura que se apoya en un proceso de modelado distribuido que se ha definido en el marco de esta investigación. Éste se basa en el aprovechamiento de recursos que distintas entidades (fuentes de datos) ofrecen para generar modelos de usuario enriquecidos que cubren las necesidades específicas de terceras entidades, considerando la participación del usuario y el control sobre sus recursos personales (datos y modelos de usuario). Lo anterior ha requerido identificar y caracterizar las fuentes de datos con potencial de abastecer al ecosistema, determinar distintos patrones para la generación de modelos de usuario a partir de datos personales distribuidos y heterogéneos y establecer una infraestructura para la gestión de identidad y privacidad que permita a los usuarios expresar sus preferencias e intereses respecto al uso y compartición de sus recursos personales. Además, se ha definido un modelo de negocio de referencia que sustenta las investigaciones realizadas y que ha sido particularizado en dos ámbitos de aplicación principales, en concreto, el sector de publicidad en redes sociales y el sector financiero para la implantación de nuevos servicios. Finalmente, cabe destacar que las contribuciones de esta tesis han sido validadas en el contexto de distintos proyectos de investigación industrial aplicada y también en el marco de proyectos fin de carrera que la autora ha tutelado o en los que ha colaborado. Los resultados obtenidos han originado distintos méritos de investigación como dos patentes en explotación, la publicación de un artículo en una revista con índice de impacto y diversos artículos en congresos internacionales de relevancia. Algunos de éstos han sido galardonados con premios de distintas instituciones, así como en las conferencias donde han sido presentados. ABSTRACT Information society services have changed most of our daily activities, offering unprecedented opportunities with certain characteristics, such as: ubiquitous access, permanent availability, device independence, multimodality and free-of-charge services, among others. However, all the positive aspects that emerge when thinking about these services have as counterpart not-so-obvious threats and risks, because they feed from and use personal data, thus creating concerns about peoples’ privacy. Nowadays, people that play the role of user of services are constantly generating digital data in different service providers. These data reflect part of their intimacy, particular characteristics, preferences, interests, relationships, consumer behavior, etc. Controversy arises because this personal information is stored and kept by the mentioned providers that can use it beyond the user needs and control. Personal data and, in particular, the knowledge about the user that can be obtained from them (user models) have turned into a new economic asset for the service providers. In this way, these data and models can be used to offer user centric services based, for example, in content recommendation, tailored-products or user behavior, all of which allows connecting with the users, keeping them more engaged and involved with the provider, finally reaching customer loyalty in order to guarantee the success of a business model. However, these resources can be used to establish a different kind of business model; one that does not only processes and individually applies personal data, but also shares and trades these data with other entities. From that perspective, the users lack control over their referred data, because it depends from the conditions imposed by the service providers. The consequence is that the users often face the following dilemma: either giving up their personal data or not using the offered services. The Public Sector takes actions in order to protect the users approving, for example, laws and legal initiatives that reinforce privacy and increase control over personal data, while at the same time the authorities are also key players in the economy development that derives from the information society services. In this context, this PhD Dissertation proposes an architecture and reference model to achieve a user-centric personal data ecosystem that promotes the creation, sharing and use of personal data and user models among different providers, while offering users the tools to control who can access which data and why and if applicable, to obtain different incentives. The original contributions obtained are the specification and design of an architecture that supports a distributed user modelling process defined by this research. This process is based on leveraging scattered resources of heterogeneous entities (data sources) to generate on-demand enriched user models that fulfill individual business needs of third entities, considering the involvement of users and the control over their personal resources (data and user models). This has required identifying and characterizing data sources with potential for supplying resources, defining different generation patterns to produce user models from scattered and heterogeneous data, and establishing identity and privacy management infrastructures that allow users to set their privacy preferences regarding the use and sharing of their resources. Moreover, it has also been proposed a reference business model that supports the aforementioned architecture and this has been studied for two application fields: social networks advertising and new financial services. Finally, it has to be emphasized that the contributions obtained in this dissertation have been validated in the context of several national research projects and master thesis that the author has directed or has collaborated with. Furthermore, these contributions have produced different scientific results such as two patents and different publications in relevant international conferences and one magazine. Some of them have been awarded with different prizes.

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our data analytics tool based on k-means clustering and on SSE, silhouette, Dunn index and Xi-Beni index quality metrics. Experiments performed on a dataset obtained from our online e-learning platform show that the extracted interaction patterns were representative in classifying learners. Furthermore, the performed monitoring activities created a strong basis for generating automatic feedback to learners in terms of their course participation, while relying on their previous performance. In addition, our analysis introduces automatic triggers that highlight learners who will potentially fail the course, enabling tutors to take timely actions.

Computational approaches to emotional decision making

Findings on the role that emotion plays in human behavior have transformed Artificial Intelligence computations. Modern research explores how to simulate more intelligent and flexible systems. Several studies focus on the role that emotion has in order to establish values for alternative decision and decision outcomes. For instance, Busemeyer et al. (2007) argued that emotional state affects the subjectivity value of alternative choice. However, emotional concepts in these theories are generally not defined formally and it is difficult to describe in systematic detail how processes work. In this sense, structures and processes cannot be explicitly implemented. Some attempts have been incorporated into larger computational systems that try to model how emotion affects human mental processes and behavior (Becker-Asano & Wachsmuth, 2008; Marinier, Laird & Lewis, 2009; Marsella & Gratch, 2009; Parkinson, 2009; Sander, Grandjean & Scherer, 2005). As we will see, some tutoring systems have explored this potential to inform user models. Likewise, dialogue systems, mixed-initiative planning systems, or systems that learn from observation could also benefit from such an approach (Dickinson, Brew & Meurers, 2013; Jurafsky & Martin, 2009). That is, considering emotion as interaction can be relevant in order to explain the dynamic role it plays in action and cognition (see Boehner et al., 2007).

Green-Markov models - new optimization strategies: a case study for user allocation in co-channel macro/femto networks

ABSTRACT: The femtocell concept aims to combine fixed-line broadband access with mobile telephony using the deployment of low-cost, low-power third and fourth generation base stations in the subscribers' homes. While the self-configuration of femtocells is a plus, it can limit the quality of service (QoS) for the users and reduce the efficiency of the network, based on outdated allocation parameters such as signal power level. To this end, this paper presents a proposal for optimized allocation of users on a co-channel macro-femto network, that enable self-configuration and public access, aiming to maximize the quality of service of applications and using more efficiently the available energy, seeking the concept of Green networking. Thus, when the user needs to connect to make a voice or a data call, the mobile phone has to decide which network to connect, using the information of number of connections, the QoS parameters (packet loss and throughput) and the signal power level of each network. For this purpose, the system is modeled as a Markov Decision Process, which is formulated to obtain an optimal policy that can be applied on the mobile phone. The policy created is flexible, allowing different analyzes, and adaptive to the specific characteristics defined by the telephone company. The results show that compared to traditional QoS approaches, the policy proposed here can improve energy efficiency by up to 10%.