An Adaptive Cross-Site User Modelling Platform for Information Exchange Techniques

The objective of the thesis is to build an adaptive Cross Site User Modelling platform for information exchange techniques in order to identify and evaluate such information exchange mechanisms. The information exchange mechanisms provide useful information to target websites that can use it to personalise the user browsing experience

Una arquitectura para sistemas de ubicación

Los modelos de ubicación se desarrollan con el fin de expresar las relaciones físicas entre los objetos. Como todo modelo, puede ocurrir que surjan nuevos requerimientos luego de que este fue puesto en producción y que estos provoquen modificaciones en el modelo original. Dependiendo de cuán correcto y con qué fin fue diseñado, estos cambios pueden provocar que el modelo quede obsoleto y se tenga que rediseñar. Los modelos de ubicación existentes (Capítulo 2 y 3) son adecuados dependiendo del tipo de aplicación que estemos desarrollando. Por lo tanto debemos conocer con anterioridad cuales son los requerimientos que esta posee para tomar la decisión de cual de ellos elegir. Si tomamos en cuenta que con la aparición de los dispositivos móviles los requerimientos pueden ser aún más cambiantes y que los modelos de ubicaciones podrán ser utilizados por múltiples aplicaciones, es probable que en un tiempo menor al esperado nuestro modelo quede obsoleto. Los modelos actuales intentan modelar a la ubicación como algo formalizable lo cual provoca que los mecanismos para agregar semántica a las ubicaciones no sean tomados en cuenta. Por este motivo, el primer objetivo que tiene este trabajo es el de proveer un modelo que permita enriquecer semánticamente a las ubicaciones de manera que los problemas de escalabilidad encontrados en las implementaciones actuales desaparezcan. De esta manera se permitirá que un modelo evolucione agregando semántica mediante distintas representaciones de una misma ubicación.Facultad de Informátic

Event and map content personalisation in a mobile and context-aware environment.

Effective methods for information access are of the greatest importance for our modern lives “ particularly with respect to handheld devices. Personalisation is one such method which models a users characteristics to deliver content more focused to the users needs. The emerging area of sophisticated mobile computing devices has started to inspire new forms of personalised systems that include aspects of the persons contextual environment. This thesis seeks to understand the role of personalisation and context, to evaluate the effectiveness of context for content personalisation and to investigate the event and map content domain for mobile usage. The work presented in this thesis has three parts: The first part is a user experiment on context that investigated the contextual attributes of time, location and interest, with respect to participants perception of their usefulness. Results show highly dynamic and interconnected effects of context on participants usefulness ratings. In the second part, these results were applied to create a predictive model of context that was related to attribution theory and then combined with an information retrieval score to create a weighted personalisation model. In the third part of this work, the personalisation model was applied in a mobile experiment. Participants solved situational search tasks using a (i) non-personalized and a (ii) personalized mobile information system, and rating entertainment events based on usefulness. Results showed that the personalised system delivered about 20% more useful content to the mobile user than the non-personalised system, with some indication for reduced search effort in terms of time and the amount of queries per task. The work presented provides evidence for the promising potential of context to facilitate personalised information delivery to users of mobile devices. Overall, it serves as an example of an investigation into the effectiveness of context from multiple angles and provides a potential link to some of the aspects of psychology as a potential source for a deeper understanding of contextual processes in humans

Sistema de localización de dispositivos móviles basada en wireless LAN

En los últimos tiempos se ha podido apreciar un gran auge en las tecnologías inalámbricas, así como una gran incursión en el mercado de los servicios de información y comunicaciones de los servicios de localización. Por ello surgen diversos proyectos de sistemas de localización en interiores, tanto en entornos de investigación, como sistemas comerciales. Estos sistemas son totalmente cerrados, no se puede modificar el algoritmo de localización que se haya elegido. Además en código abierto solo se pueden apreciar sistemas sobre Linux/Unix, cuando una gran parte de los dispositivos inalámbricos poseen S.O. de Microsoft. Este Proyecto Fin de Carrera aborda la construcción de un Sistema de Posicionamiento totalmente escalable, que permita realizar localización de dispositivos en interiores con redes locales inalámbricas en el que se puedan evaluar distintos algoritmos de localización, al ser un sistema totalmente modular. El resultado final de este Proyecto Fin de Carrera es un Sistema de Posicionamiento para cualquier entorno de interior (en casa, en la oficina, en universidades, en Centros Comerciales) que disponga de cobertura de red local inalámbrica (WLAN). El sistema permitirá tanto localizarse como ser localizado. Es decir, una persona que disponga de este sistema podrá situarse dentro de un edificio en el que no conozca su situación, y, a su vez, permitir ser localizado dentro del mismo. Dicho sistema podrá ser ejecutado en cualquier sistema operativo, ya sea Microsoft Windows o Linux/Unix, debido a su portabilidad. También podrá cambiarse el algoritmo de localización gracias a su modularidad. _________________________________________________________Nowadays wireless technologies have experienced a great expansion, and they have conquered the market of information and communications services within the location services. Hence, a plethora of indoor location system projects arises, both in research environments and in commercial systems. These systems are completely closed. Their main drawback is the fact that the location algorithm can not be changed. In addition, open source systems are usually developed for Linux/Unix, although most of the wireless devices have Microsoft Operating System. The Project addresses the construction of a positioning system fully scalable; the goal is to locate wireless devices in indoor environments. The user or operator is given the chance to select different location algorithms in a fully modular system. The result of this Project is a positioning system for any indoor environment (at home, at the office, in the universities, at the commercial centers) that provide coverage of Wireless LAN (WLAN). The system allows the device to be located and to obtain its own position. For example, a person who has this system may be located within a building that does not know her/his situation, and, in turn, permits to be located within it. That system may be run on any operating system, either Microsoft Windows or Linux/Unix, because of its portability. The algorithm for locating devices may be easily changed due to the modularity of the system.Ingeniería de Telecomunicació

Personalized information retrieval based on time-sensitive user profile

Les moteurs de recherche, largement utilisés dans différents domaines, sont devenus la principale source d'information pour de nombreux utilisateurs. Cependant, les Systèmes de Recherche d'Information (SRI) font face à de nouveaux défis liés à la croissance et à la diversité des données disponibles. Un SRI analyse la requête soumise par l'utilisateur et explore des collections de données de nature non structurée ou semi-structurée (par exemple : texte, image, vidéo, page Web, etc.) afin de fournir des résultats qui correspondent le mieux à son intention et ses intérêts. Afin d'atteindre cet objectif, au lieu de prendre en considération l'appariement requête-document uniquement, les SRI s'intéressent aussi au contexte de l'utilisateur. En effet, le profil utilisateur a été considéré dans la littérature comme l'élément contextuel le plus important permettant d'améliorer la pertinence de la recherche. Il est intégré dans le processus de recherche d'information afin d'améliorer l'expérience utilisateur en recherchant des informations spécifiques. Comme le facteur temps a gagné beaucoup d'importance ces dernières années, la dynamique temporelle est introduite pour étudier l'évolution du profil utilisateur qui consiste principalement à saisir les changements du comportement, des intérêts et des préférences de l'utilisateur en fonction du temps et à actualiser le profil en conséquence. Les travaux antérieurs ont distingué deux types de profils utilisateurs : les profils à court-terme et ceux à long-terme. Le premier type de profil est limité aux intérêts liés aux activités actuelles de l'utilisateur tandis que le second représente les intérêts persistants de l'utilisateur extraits de ses activités antérieures tout en excluant les intérêts récents. Toutefois, pour les utilisateurs qui ne sont pas très actifs dont les activités sont peu nombreuses et séparées dans le temps, le profil à court-terme peut éliminer des résultats pertinents qui sont davantage liés à leurs intérêts personnels. Pour les utilisateurs qui sont très actifs, l'agrégation des activités récentes sans ignorer les intérêts anciens serait très intéressante parce que ce type de profil est généralement en évolution au fil du temps. Contrairement à ces approches, nous proposons, dans cette thèse, un profil utilisateur générique et sensible au temps qui est implicitement construit comme un vecteur de termes pondérés afin de trouver un compromis en unifiant les intérêts récents et anciens. Les informations du profil utilisateur peuvent être extraites à partir de sources multiples. Parmi les méthodes les plus prometteuses, nous proposons d'utiliser, d'une part, l'historique de recherche, et d'autre part les médias sociaux. En effet, les données de l'historique de recherche peuvent être extraites implicitement sans aucun effort de l'utilisateur et comprennent les requêtes émises, les résultats correspondants, les requêtes reformulées et les données de clics qui ont un potentiel de retour de pertinence/rétroaction. Par ailleurs, la popularité des médias sociaux permet d'en faire une source inestimable de données utilisées par les utilisateurs pour exprimer, partager et marquer comme favori le contenu qui les intéresse. En premier lieu, nous avons modélisé le profil utilisateur utilisateur non seulement en fonction du contenu de ses activités mais aussi de leur fraîcheur en supposant que les termes utilisés récemment dans les activités de l'utilisateur contiennent de nouveaux intérêts, préférences et pensées et doivent être pris en considération plus que les anciens intérêts surtout que de nombreux travaux antérieurs ont prouvé que l'intérêt de l'utilisateur diminue avec le temps. Nous avons modélisé le profil utilisateur sensible au temps en fonction d'un ensemble de données collectées de Twitter (un réseau social et un service de microblogging) et nous l'avons intégré dans le processus de reclassement afin de personnaliser les résultats standards en fonction des intérêts de l'utilisateur.En second lieu, nous avons étudié la dynamique temporelle dans le cadre de la session de recherche où les requêtes récentes soumises par l'utilisateur contiennent des informations supplémentaires permettant de mieux expliquer l'intention de l'utilisateur et prouvant qu'il n'a pas trouvé les informations recherchées à partir des requêtes précédentes.Ainsi, nous avons considéré les interactions récentes et récurrentes au sein d'une session de recherche en donnant plus d'importance aux termes apparus dans les requêtes récentes et leurs résultats cliqués. Nos expérimentations sont basés sur la tâche Session TREC 2013 et la collection ClueWeb12 qui ont montré l'efficacité de notre approche par rapport à celles de l'état de l'art. Au terme de ces différentes contributions et expérimentations, nous prouvons que notre modèle générique de profil utilisateur sensible au temps assure une meilleure performance de personnalisation et aide à analyser le comportement des utilisateurs dans les contextes de session de recherche et de médias sociaux.Recently, search engines have become the main source of information for many users and have been widely used in different fields. However, Information Retrieval Systems (IRS) face new challenges due to the growth and diversity of available data. An IRS analyses the query submitted by the user and explores collections of data with unstructured or semi-structured nature (e.g. text, image, video, Web page etc.) in order to deliver items that best match his/her intent and interests. In order to achieve this goal, we have moved from considering the query-document matching to consider the user context. In fact, the user profile has been considered, in the literature, as the most important contextual element which can improve the accuracy of the search. It is integrated in the process of information retrieval in order to improve the user experience while searching for specific information. As time factor has gained increasing importance in recent years, the temporal dynamics are introduced to study the user profile evolution that consists mainly in capturing the changes of the user behavior, interests and preferences, and updating the profile accordingly. Prior work used to discern short-term and long-term profiles. The first profile type is limited to interests related to the user's current activities while the second one represents user's persisting interests extracted from his prior activities excluding the current ones. However, for users who are not very active, the short-term profile can eliminate relevant results which are more related to their personal interests. This is because their activities are few and separated over time. For users who are very active, the aggregation of recent activities without ignoring the old interests would be very interesting because this kind of profile is usually changing over time. Unlike those approaches, we propose, in this thesis, a generic time-sensitive user profile that is implicitly constructed as a vector of weighted terms in order to find a trade-off by unifying both current and recurrent interests. User profile information can be extracted from multiple sources. Among the most promising ones, we propose to use, on the one hand, searching history. Data from searching history can be extracted implicitly without any effort from the user and includes issued queries, their corresponding results, reformulated queries and click-through data that has relevance feedback potential. On the other hand, the popularity of Social Media makes it as an invaluable source of data used by users to express, share and mark as favorite the content that interests them. First, we modeled a user profile not only according to the content of his activities but also to their freshness under the assumption that terms used recently in the user's activities contain new interests, preferences and thoughts and should be considered more than old interests. In fact, many prior works have proved that the user interest is decreasing as time goes by. In order to evaluate the time-sensitive user profile, we used a set of data collected from Twitter, i.e a social networking and microblogging service. Then, we apply our re-ranking process to a Web search system in order to adapt the user's online interests to the original retrieved results. Second, we studied the temporal dynamics within session search where recent submitted queries contain additional information explaining better the user intent and prove that the user hasn't found the information sought from previous submitted ones. We integrated current and recurrent interactions within a unique session model giving more importance to terms appeared in recently submitted queries and clicked results. We conducted experiments using the 2013 TREC Session track and the ClueWeb12 collection that showed the effectiveness of our approach compared to state-of-the-art ones. Overall, in those different contributions and experiments, we prove that our time-sensitive user profile insures better performance of personalization and helps to analyze user behavior in both session search and social media contexts

A mobile context-aware learning schedule framework with Java learning objects

The focus of this thesis is the study of mobile learning, specifically learning in different locations and under various contextual situations, from the perspective of university students. I initially derived and designed a theoretical mobile context-aware learning schedule (mCALS) framework from an extensive literature review. Its objective is to recommend appropriate learning materials to students based on their current locations and circumstances. The framework uses a learning schedule (i.e. electronic-based diary) to inform the location and available time a student has for learning/studying at a particular location. Thereafter, a number of factors are taken into consideration for the recommendation of appropriate learning materials. These are the student’s learning styles, knowledge level, concentration level, frequency of interruption at that location and their available time for learning/studying. In order to determine the potential deployment of the framework as a mobile learning application by intended users, I carried out three types of feasibility studies. First, a pedagogical study was conducted using interviews to explore together with students (a) what their learning requirements were when studying in a mobile environment, (b) whether the framework could potentially be used effectively to support their studies and, (c) using this user-centred understanding, refined user requirements of the framework. Second, a diary study was conducted where I collected data and analysed the usability feasibility of the framework by (a) determining whether students could plan their daily schedule ahead and keep to it, (b) ascertaining which learning contexts were important and, (c) establishing which learning materials were appropriate under which situations. Two validation studies were conducted. The first one was an online experiment utilising Java learning objects. Participants of this study were suggested appropriate learning objects to study with, based on their amount of available time, current motivation level for learning and their proficiency level of Java. The second validation study was an investigation into high-quality Java learning objects available in the public domain. Finally, a technical design of the framework was carried out to determine whether the framework at present could realistically be implemented using current mobile technologies. The data analyses of the feasibility studies show that (a) a learning schedule approach is successful to an extent in obtaining location and available time information to indicate accurate values of these contexts, (b) different learners may require different personalisation strategies when selecting appropriate learning materials for them in mobile environments, and (c) the mCALS framework is particularly well-suited for self-regulated students. I also proposed a set of suggestion rules which can be used to recommend appropriate Java learning materials to students in different contexts. The validation studies show that 1) the proposed suggestion rules are effective in recommending appropriate materials to learners in their situation, in order to enhance their learning experiences, and 2) there are a sufficiently large number of high-quality LOs available in the public domain that can be incorporated for use within my framework. Finally, the development of mCALS has been considered from three perspectives – pedagogical, usability and technical. These perspectives consist of critical components that should be considered when developing and evaluating mobile learning software applications. The results demonstrated that the mCALS framework can potentially be used by students in different locations and situations, and appropriate learning materials can be selected to them, in order to enhance their learning experiences.EThOS - Electronic Theses Online ServiceGBUnited Kingdo