Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-10-03T12:30:12Z No. of bitstreams: 1 Fernando Scheid_.pdf: 1316185 bytes, checksum: 82e933449d1319408b251dc78485aee2 (MD5) / Made available in DSpace on 2017-10-03T12:30:13Z (GMT). No. of bitstreams: 1 Fernando Scheid_.pdf: 1316185 bytes, checksum: 82e933449d1319408b251dc78485aee2 (MD5) Previous issue date: 2017-07-18 / Nenhuma / Este trabalho tem por objetivo realizar umestudo sobre o conhecimento e a utilização de bases de dados em pesquisas por parte dos alunos da graduação e pós-graduação do Instituto Federal Sul-rio-grandense Câmpus Charqueadas, tendo como cenário principal a Biblioteca com a finalidade de compreender o perfil de pesquisa desses usuários. A metodologia utilizada foi um estudo de caso e como instrumentos de coleta de dados, dois questionários: o primeiro questionário com questões fechadas incluindo comentários e sugestões e outro questionário, posteriormente, com perguntas abertas. Após a análise da coleta de dados chegou-se à conclusão de que muitos usuários desconhecem ou não utilizam bases de dados (51%) para realizarem suas pesquisas, porém as bases de dados mais conhecidas são Scielo, Portal de Periódicos da Capes e Web of Science. O curso de pós-graduação utiliza mais esses recursos em relação aos cursos de graduação, apesar de 19% afirmarem que desconhecem ou não utilizam bases de dados. Entre os sites de busca na internet o Google teve o maior destaque, com 65%. Como sugestão dos usuários está a realização de capacitações e treinamentos para que essas fontes de informação possam ser utilizadas com maior frequência. Por fim, esta pesquisa apresenta dois produtos: o diagnóstico com relação ao conhecimento e utilização de bases de dados em pesquisa pelos alunos do IFSul Câmpus Charqueadas e a proposta de um projeto de tutorial relativo à utilização de bases de dados em pesquisas para ser disponibilizado na página da Biblioteca, a fim de qualificar a busca por informações a partir de fontes online, oferecendo outra possibilidade de acesso a acervos. / This work aims to carry out a study about the knowledge and usage of data bases in researches done by the Graduation and Post-Graduation students of the Federal Institute Sul-rio-grandense, Campus Charqueadas, having the Library as the main scenario to understand the research profile of those users. The methodology used was a case study and the instruments of data collection were two questionnaires: the first questionnaire with closed questions including comments and suggestions and another questionnaire, used later, with open questions. After analyzing the data collection, it was concluded that many users are unaware or do not use databases (51%) to carry out their researches, but the most well-known databases are Scielo, Periódicos Capes and Web of Science. The postgraduate course uses these resources more in relation to undergraduate courses, although 19% state that they do not know or do not use databases. Among the search engines on the internet, Google had the biggest highlight, with 65%. As a suggestion of the users, the execution of training courses about these sources of information can be used more frequently. Finally, this research presents two products: the diagnosis regarding the knowledge and use of databases in research by the students of IFSul Campus Charqueadas and the proposal of a project of tutorial on the use of databases in researches to be available in Library website in order to qualify the search for information from online sources, offering another possibility of access to collections.

A acomodação da diversidade de perfil de usuários no projeto de interface de sistemas é um problema freqüente nas atividades do projetista da interação homem-computador. Conseqüentemente, o usuário encontra barreiras ao utilizar interfaces que não foram produzidas para o seu perfil. Este trabalho apresenta uma solução, destinada aos projetistas de famílias de sistemas interativos, para a acomodação e expressão da diversidade por meio da criação e uso de padrões de interface em camadas de personas - as PICAPs. Neste conceito, os padrões de interface apóiam o projetista no direcionamento de soluções, pois representam um problema recorrente e uma solução abstrata para o problema, de tal modo que esta solução pode ser aplicada em várias instâncias diferentes do mesmo problema. As personas apóiam a caracterização dos perfis dos usuários que são foco do projeto de interface, possibilitando que o projetista aplique soluções de interface de acordo com o usuário. O conceito foi aplicado no contexto de governo eletrônico, pois tais sistemas devem ser usáveis por todos, em distinção de qualquer natureza, sendo assim um exemplo da necessidade de se considerar a diversidade. Uma pesquisa com 25 projetistas foi feita para avaliação da aplicabilidade do conceito. / Accommodating users\' profile diversity in systems interface projects is a frequent problem for the human computer interface designer. Therefore, his/her user is faced with barriers in the use of interfaces which were not designed for his/her profile. This work presents a solution for expressing and accommodating users\' diversity, which is useful for the HCI designer, especially for those who design families of products. PICAPS are interface design patterns with layers indexed by personas. The interface design patterns support the designer in employing proven solutions, for they represent a recurrent problem and its abstract solution in such a way that this solution can be applied to different instantiations of the same problem. PICAPs are structured in multiple layers to make possible the users\' diversity accommodation. The layers are indexed by personas as user\'s characterization resource. This concept has been applied to electronic government services, because such systems should be usable by any citizen and therefore are a good example of the user diversity problem. A field research with 25 designers has been performed to check the concept´s applicability.

In this thesis we propose semantic-social recommendation algorithms, that recommend an input item to users connected by a collaboration social network. These algorithms use two types of information: semantic information and social information.The semantic information is based on the semantic relevancy between users and the input item; while the social information is based on the users position and their type and quality of connections in the collaboration social network. Finally, we use depth-first search and breath-first search strategies to explore the graph.Using the semantic information and the social information, in the recommender system, helps us to partially explore the social network, which leads us to reduce the size of the explored data and to minimize the graph searching time.We apply our algorithms on real datasets: MovieLens and Amazon, and we compare the accuracy an the performance of our algorithms with the classical recommendation algorithms, mainly item-based collaborative filtering and hybrid recommendation.Our results show a satisfying accuracy values, and a very significant performance in execution time and in the size of explored data, compared to the classical recommendation algorithms.In fact, the importance of our algorithms relies on the fact that these algorithms explore a very small part of the graph, instead of exploring all the graph as the classical searching methods, and still give a good accuracy compared to the other classical recommendation algorithms. So, minimizing the size of searched data does not badly influence the accuracy of the results.

Αυτή η εργασία περιγράφει το όραμα των I-centric επικοινωνιών (ατομοκεντρικών) - ένα νέο παράδειγμα για μελλοντικά συστήματα τηλεπικοινωνιών. Ο κύριος στόχος αυτής της προσέγγισης είναι να εξετάσει την ανθρώπινη επικοινωνιακή συμπεριφορά. Η εργασία αυτή εστιάζει στην εννοιολογική ολοκλήρωση όλων των πτυχών των ατομοκεντρικών επικοινωνιών. Εξετάζοντας την επικοινωνιακή συμπεριφορά του ανθρώπου, είναι προφανές, ότι οι άνθρωποι συχνά αλληλεπιδρούν με ένα σύνολο αντικειμένων στο περιβάλλον τους. Σύμφωνα με αυτήν την άποψη, μια προσέγγιση είναι να μη δημιουργηθούν συστήματα επικοινωνίας που βασίζονται σε εξειδικευμένες τεχνολογίες αλλά να βασίζονται στην ανάλυση του μεμονωμένου χώρου επικοινωνίας. Το αποτέλεσμα θα είναι ένα σύστημα επικοινωνιών που προσαρμόζεται στις απαιτήσεις κάθε ατόμου (ατομοκεντρικό). Το σύστημα επικοινωνιών θα ενεργεί σύμφωνα με τις απαιτήσεις του ανθρώπου, που απεικονίζουν τις πρόσφατες ενέργειές του ώστε να προσαρμόζεται αυτόματα σε αυτές. Οι ατομοκεντρικές υπηρεσίες προσαρμόζονται στους μεμονωμένους χώρους επικοινωνίας και τις καταστάσεις. Σε αυτό το πλαίσιο το «I», σημαίνει το εγώ ή το άτομο, και Centric σημαίνει προσαρμογή στις απαιτήσεις του «εγώ» και το περιβάλλον του ατόμου. Οι παραπάνω λογικές απαιτούν νοημοσύνη στην παροχή υπηρεσιών προκειμένου να προσωποποιηθούν, να προσαρμοστούν στις περιστασιακές και περιβαλλοντικές συνθήκες και να ελέγχουν το μεμονωμένο χώρο επικοινωνίας. Ένα ατομοκεντρικό σύστημα επικοινωνιών παρέχει τη νοημοσύνη που απαιτείται για τη διαμόρφωση του χώρου επικοινωνίας του κάθε ατόμου που προσαρμόζεται στα ενδιαφέροντα, το περιβάλλον, και τις προτιμήσεις του. Η εργασία εισάγει το όραμα των ατομοκεντρικών επικοινωνιών, που ακολουθείται από την ανάπτυξη ενός προτύπου αναφοράς για τις ατομοκεντρικές επικοινωνίες. Από τη στιγμή που το όραμα και το πρότυπο αναφοράς είναι γενικές έννοιες, εισάγεται στη συνέχεια ένα αρχιτεκτονικό πλαίσιο για τις ατομοκεντρικές επικοινωνίες. Αυτό το πλαίσιο χρησιμοποιείται για να σχεδιαστεί και να υλοποιηθεί ένα ατομοκεντρικό σύστημα επικοινωνιών. / This thesis describes the vision of I-centric communications – a new paradigm for future telecommunication systems. The main objective of this approach is to consider the human communication behaviour. The focus of this thesis is on the conceptual integration of all aspects of I-centric communications. Looking at the communication behaviour of human, it is obvious, that human beings frequently interact with a set of objects in their environment. Following this view, a new approach is not to build communication systems based on specific technologies, but on the analysis of the individual communication space. The result is a communication system that adapts to the demands of each individual (I-centric). The communication system will act on behalf of human’s demands, reflecting recent actions to enable self-adaptation. I-centric Services adapt to individual communication spaces and situations. In this context ‘I’ means I, or individual, ‘Centric’ means adaptable to I requirements and the individual’s environment. The rationales above require intelligence in service provisioning in order to personalize, adapt to situational and environmental conditions, to monitor and to control the individual communication space. I-centric communications system will provide the intelligence required for modelling the communication space of each individual adapting to its interests, environment, and preferences. The thesis introduces the vision of I-centric communications, followed by the development of a reference model for I-centric communications. Since both, the vision and the reference model, are general, an architectural framework for I-centric communications is introduced later on. This framework is used to design and implement an I-centric communications system.

L’utilisation de préférences suscite un intérêt croissant pour personnaliser des réponses aux requêtes et effectuer des recommandations ciblées. Pourtant, la construction manuelle de profils de préférences reste à la fois complexe et consommatrice de temps. Dans ce contexte, nous présentons dans cette thèse une nouvelle méthode automatique d’extraction de préférences basée sur des techniques de fouille de données. L’approche que nous proposons est constituée de deux phases : (1) une phase d’extraction de toutes les règles de préférences contextuelles intéressantes et (2) une phase de construction du profil utilisateur. A la fin de la première phase, nous constatons qu’il y a des règles redondantes voir superflues ; la seconde phase permet d’éliminer les règles superflues afin d’avoir un profil concis et consistant. Dans notre approche, un profil utilisateur est constitué de cet ensemble de règles de préférences contextuelles résultats de la seconde phase. La consistance garantit que les règles de préférences spécifiant les profils sont en accord avec un grand nombre de préférences utilisateur et contredisent un petit nombre d’entre elles. D’autre part, la concision implique que les profils sont constitués d’un petit nombre de règles de préférences. Nous avons aussi proposé quatre méthodes de prédiction qui utilisent les profils construits. Nous avons validé notre approche sur une base de données de films construite à partir de MovieLens et IMDB. La base de données contient 3 881 films notés par 6 040 utilisateurs. Ces derniers ont attribué 800 156 notes. Les résultats de ces expériences démontrent que la concision des profils utilisateurs est contrôlée par le seuil d’accord minimal et que même avec une forte réduction du nombre de règles, les qualités de prédiction des profils restent à un niveau acceptable. En plus des expérimentations montrant la qualité de prédiction de notre approche, nous avons montré également que les performances de notre approche peuvent rivaliser avec les qualités de prédiction de certaines méthodes de l’état de l’art, en particulier SVMRank. / The use of preferences arouses a growing interest to personalize response to requests and making targeted recommandations. Nevertheless, manual construction of preferences profiles remains complex and time-consuming. In this context, we present in this thesis a new automatic method for preferences elicitation based on data mining techniques. Our proposal is a two phase algorithm : (1) Extracting all contextual preferences rules from a set of user preferences and (2) Building user profile. At the end of the first phase, we notice that there is to much preference rules which satisfy the fixed constraints then in the second phase we eliminate the superfluous preferences rules. In our approach a user profile is constituted by the set of contextual preferences rules resulting of the second phase. A user profile must satisfy conciseness and soundness properties. The soundness property guarantees that the preference rules specifying the profiles are in agreement with a large set of the user preferences, and contradict a small number of them. On the other hand, conciseness implies that profiles are small sets of preference rules. We also proposed four predictions methods which use the extracted profiles. We validated our approach on a set of real-world movie rating datasets built from MovieLens and IMDB. The whole movie rating database consists of 800,156 votes from 6,040 users about 3,881 movies. The results of these experiments demonstrates that the conciseness of user profiles is controlled by the minimal agreement threshold and that even with strong reduction, the soundness of the profile remains at an acceptable level. These experiment also show that predictive qualities of some of our ranking strategies outperform SVMRank in several situations.

Le développement rapide des TIC (Information and Communication Technology) a un impact énorme à toutes les industries. Internet Mobile, Web 2.0, la technologie de positionnement sont déployées dans le monde largement, qui a initialisé la demande intérieure des nouvelles technologies dans le domaine du tourisme. En profitant de la commodité apportée par les nouvelles technologies, de plus en plus d'utilisateurs de commencer à se concentrer sur les questions de confidentialité.Comme on le sait, il y a une contradiction entre la qualité des services et la vie privée recommandés basés sur la localisation. profil de l'utilisateur détaillé et informations de localisation précises sont nécessaires pour fournir un service basé sur la localisation avec une grande efficacité et de qualité, tandis que la préservation de la vie privée exige cacher le profil et l'emplacement de l'utilisateur. De nombreuses recherches récentes vise à trouver un équilibre entre eux, pour obtenir la meilleure qualité de la LBS dans le contexte du contenu du profil de l'utilisateur et les informations de localisation moins exposée.Dans cette thèse, l'auteur a porté principalement sur la vie privée de profil de l'utilisateur mobile, qui couvre à la fois les caractéristiques personnelles et les informations de localisation.1, sur mesure modèle de confidentialité des utilisateurs considérant à la fois les qualités personnelles et la localisation spatiale et temporelle est définie pour l'utilisateur mobile.Dans cette recherche, nous avons proposé le modèle de confidentialité personnalisé pour chaque utilisateur de définir, son / sa propre signification de la vie privée. Avec ce modèle, à des fins de Voyage différents, ou même pour le temps et un autre emplacement, ils peuvent avoir des définitions différentes pour leur vie privée.2, Minimiser les données à stocker sur le serveur.Les profils sont stockés de deux façons différentes, le profil complet est stocké sur le côté client, et seules les valeurs des attributs qui sont en mesure pour les utilisateurs de partager sont stockés sur le côté serveur, ainsi minimisé les données à stocker sur le serveur.3, les niveaux personnalisés de granularité de la divulgation de l'emplacement et l'heure sont réglables pour les utilisateurs.Pour changer fréquemment des attributs tels que l'emplacement et l'heure, l'utilisateur peut les divulguer avec une granularité appropriée, afin d'obtenir le service attendu par les fournisseurs de services.Mots-clés: vie privée, l'emplacement, le profil de l'utilisateur, mobile, tourisme / The rapid development of ICT(Information and Communication Technology) brought huge impact to all industries. Mobile internet, Web 2.0, positioning technology are deployed world-widely, that initialized inner request of new technologies in the field of tourism as well. By enjoying the convenience brought by new technologies, more and more users starting to focus on privacy issues.As known, there is a contradiction between the quality of recommended location-based services and privacy. Detailed user profile and precise location information are needed for providing location-based service with high efficiency and quality, while privacy preservation requires hiding user's profile and location. Many recent researches aims to seek a balance between them, to obtain the best quality of the LBS in the context of the least exposed user profile content and location information.In this thesis, the author focused mainly on the privacy of mobile user profile, which covers both personal characteristics and location information.1, Customized user privacy model considering both personal attributes and spatial and temporal location is defined for mobile user.In this research, we proposed customized privacy model for every user to define, his/her own meaning of privacy. With this model, for different travel purpose, or even for different time and location, they can have different definitions for their privacy.2, Minimized the data to be stored on server.Profiles are stored in two different ways, complete profile is stored on the client side, and only the values of the attributes that are able for the users to share are stored on the server side, thus minimized the data to be stored on server.3, Customized levels of granularity of disclosure of location and time are adjustable for users.For frequently changing attributes such as location and time, user can disclose them with suitable granularity, in order to obtain expected service from the service providers.Keywords: privacy, location, user profile, mobile, tourism

Pour pouvoir restituer des informations qui correspondent aux besoins de l'utilisateur, les mécanismes d'adaptation doivent disposer de métadonnées sur celui-ci telles que ses caractéristiques personnelles, ses préférences générales, ses centres d'intérêt. De ce fait, le profil utilisateur construit à partir de celles-ci devient central dans tout système basé sur la personnalisation. Dans cette thèse, nous nous focalisons sur l'approche qui consiste à s'appuyer sur le réseau social de l'utilisateur pour enrichir le profil de cet utilisateur, les métadonnées explicites étant complétées par les informations issues de notre processus d'analyse. Nous appelons les techniques ou processus associés à cette approche " profilage social ". Le terme " profil social " désigne un profil construit à l'aide du réseau social de l'utilisateur. Un profil social contient les métadonnées traduisant les intérêts de l'utilisateur extraits à partir des informations partagées par les individus de son réseau social. Les intérêts de l'utilisateur évoluant au fil du temps dans la vie réelle, il en est de même pour ceux extraits depuis son réseau social : pertinents à un moment donné, ils peuvent ne plus être significatifs ultérieurement. Partant de ce constat, les principales informations que nous souhaitons étudier pour détecter un changement de centres d'intérêt ne sont pas ciblées sur l'utilisateur lui-même mais sur les éléments de son réseau social (liens entre les membres, informations qui circulent entre eux) : l'évolution du profil social de l'utilisateur est donc liée à l'évolution de son réseau social. Nous proposons une démarche générique de profilage social efficace permettant de construire un profil social représentatif de l'utilisateur prenant en compte différents types de réseaux ainsi que leurs caractéristiques évolutives. Pour prendre en compte l'évolution des intérêts dans le profil social, nous avons proposé d'améliorer l'efficacité des processus de construction du profil social existants en intégrant la prise en compte de l'évolution du réseau social de l'utilisateur. Nous proposons d'intégrer un facteur temporel dans ces processus (approche basée sur des individus et approche basée sur les communautés). La solution permet de privilégier les intérêts provenant d'informations significatives et à jour. Il s'agit donc d'intégrer une mesure temporelle dans l'étape d'extraction et pondération des intérêts. Cette mesure est calculée d'une part, à partir de la pertinence temporelle des informations utilisées pour extraire cet intérêt et d'autre part, à partir de la pertinence temporelle de l'individu qui partage ces informations. Nous mettons en œuvre la méthode proposée au travers d'expérimentations dans deux réseaux sociaux différents : DBLP, un réseau de publications scientifiques et Twitter, un réseau de micro-blogs. Les résultats de ces expérimentations nous ont permis de montrer l'efficacité de la méthode temporelle proposée par rapport aux processus de construction du profil social qui ne prennent pas en compte des critères temporels. En étudiant les résultats en fonction des techniques de pondération des intérêts ou fonctions temporelles utilisées, nous constatons que la fonction temporelle et la technique utilisées donnant les meilleurs résultats varient selon l'approche de construction du profil social choisie, selon la taille et la densité du réseau étudié mais aussi selon sur le type de réseau. La problématique abordée dans cette thèse est relativement nouvelle dans le contexte des systèmes de personnalisation de l'information et ouvre de nombreuses perspectives : évaluation du profil social dans un système de recommandation par exemple, application de la méthode proposée dans d'autres types de réseaux sociaux, application de techniques de mise à jour du profil, conception d'une plateforme de construction du profil social selon les caractéristiques du réseau. / User profiling is essential for personalization systems (e.g. personalized information retrieval systems, recommendation systems) to identify user information (preference, interests...), in order to propose relevant content based on his/her specific needs and requirements. Many works have shown that user's social neighbors can be a meaningful source to infer his/her interests. Besides, sociology works have shown that the user is better described by people around him/her, especially the people that are directly connected to him/her (his egocentric network). In this work, the term "social profiling" is considered as the interest extraction approach that consists in extracting user interests from information of his/her social neighbors. The user's profile built within this approach is called "social profile". As user behaviors evolve over time, it is necessary to take into consideration the evolution of user interests in user profiling process. In the case of social profile, user interests are extracted from the information shared by his/her social neighbors. Hence, the evolution of extracted interests is related to the evolution of information shared on user social network and to the evolution of relationships between the user and his/her social neighbors. This issue becomes particularly important in the Online Social Networks (OSNs) context where user behavior changes quickly. For a user, the relationships and information in his/her social network can evolve and become obsolete for him/her overtime. Two users creating a relationship are not required to know each other in real life. Thus, the relationship persistence is not always maintained in this case. Social events or viral marketing (buzz) are also factors that enhance online social content sharing. In this work, we propose a generic approach that considers the evolution in user's social network in the social profiling process and can be applied in different types of social network. To handle this, we propose to apply a time-aware method into existing social profile building process (individual based and community based approaches). This strategy aims at weighting user's interests in the social profile based on their temporal score. The temporal score of an interest is computed by combining the temporal score of information used to extract the interests (computed by considering their freshness) with the temporal of individuals who share the information in the network (computed by considering the freshness of the interaction with the user). The technique and temporal function used to compute the temporal score are customizable. Thus, we can find out the most appropriate technique or temporal function depending on the types or characteristics of the adopted social network. The experiments conducted on DBLP and Twitter showed that the so-called time-aware social profiling process applying our proposed time-aware method outperforms the existing time-agnostic social profiling process. We also found that the most appropriate technique, temporal function and social profiling approach vary depending on the network characteristics (size, density) and to the social network type. Our approach opens many opportunities for future studies in social information filtering and many application domains as well as on the Web (e.g. evolution of social profile in personalization of search engines, recommender systems in e-commerce,). Our long-term perspective consists in the proposal of a generic platform that extracts the information and builds the user social profile based on the type and the specific characteristics of the underlying social network. Such a platform would be parameterized by the characteristics of the targeted social network using a machine learning approach.

Context. Trust and reliability are important issues in online communication. By rapid growth of online social networks (OSNs), online communication becomes richer by the integrating of social interaction into the communication model. However, E-mail communication systems concern about unsolicited messages. Objectives. In this thesis the aim is to investigate how to prioritize E-mails between recipients and senders by using information from OSNs. Methods. An algorithm is presented for computing trust by measuring users‟ interaction and similarity in online social networks and this trust is used by another algorithm for prioritizing the E-mail inbox. Results. An evaluation of the proposed method is performed via a case study and the prediction error of the method is compared with the prediction error of the random feedback. The error of the method is significantly lower than random feedback and is relatively low, given the small number of observations. Conclusions. This thesis contributes in its review and categorization of existing trust models. Furthermore, it provides an analysis on how to use social information for E-mail prioritization. Based on the analysis, a method is presented for improving the reliability of E-mail communication by extracting information from OSNs. The information is used for computing the trust score between two OSN friends. In this thesis, it is suggested that, inbox prioritization is achievable using the selected method. / This thesis has addressed E-mail prioritization through social network by using social information. The task has been done by focusing on the interaction and similarity between friends in the OSN. A theoretical analysis has been performed in order to identify the characteristic of suitable trust model. An algorithm (Algorithm 1) has been suggested to estimate weights of different criteria of social information. In order to have the trust predictions based on the user‟s preferences, the algorithm adjusted the weights based on the user‟s feedback. In addition, another algorithm (Algorithm 2) has been proposed to compute trust scores and prioritize E-mails inbox. Finally, an algorithm (Algorithm 3) has been presented to evaluate the error of the computed (predicted) trust scores. In order to display the applicability of the method as well as to motivate the theoretical foundation, a case study was reported in which the proposed method was applied to Facebook. The analysis showed that the proposed method was feasible to be used, and it provided users a mean to prioritize E-mail inboxes based on the social information extracted from Facebook. The analysis indicated that least squares method was a suitable approach to estimate weights that were used in computing trust scores and thus prioritizing E-mails inbox.

L'augmentation constante du volume d'information disponible sur le Web a rendu compliquée la découverte de nouvelles sources d'intérêt sur un sujet donné. Les experts du renseignement doivent faire face à cette problématique lorsqu'ils recherchent des pages sur des sujets spécifiques et sensibles. Ces pages non populaires sont souvent mal indexées ou non indexées par les moteurs de recherche à cause de leur contenu délicat, les rendant difficile à trouver. Nos travaux, qui s'inscrivent dans ce contenu du Renseignement d'Origine Source Ouverte (ROSO), visent à aider l'expert du renseignement dans sa tâche de découverte de nouvelles sources. Notre approche s'articule autour de la modélisation du besoin opérationnel et de l'exploration ciblée du Web. La modélisation du besoin informationnel permet de guider l'exploration du web pour découvrir et fournir des sources pertinentes à l'expert. / The constant growth of the Web in recent years has made more difficult the discovery of new sources of information on a given topic. This is a prominent problem for Expert in Intelligence Analysis (EIA) who are faced with the search of pages on specific and sensitive topics. Because of their lack of popularity or because they are poorly indexed due to their sensitive content, these pages are hard to find with traditional search engine. In this article, we describe a new Web source discovery system called DOWSER. The goal of this system is to provide users with new sources of information related to their needs without considering the popularity of a page unlike classic Information Retrieval tools. The expected result is a balance between relevance and originality, in the sense that the wanted pages are not necessary popular. DOWSER in based on a user profile to focus its exploration of the Web in order to collect and index only related Web documents.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-12T14:22:55Z No. of bitstreams: 1 crystiamkellepereiraesilva.pdf: 5368190 bytes, checksum: 22e15248de5dbc282e6d4324b03dca64 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T17:15:27Z (GMT) No. of bitstreams: 1 crystiamkellepereiraesilva.pdf: 5368190 bytes, checksum: 22e15248de5dbc282e6d4324b03dca64 (MD5) / Made available in DSpace on 2016-01-25T17:15:27Z (GMT). No. of bitstreams: 1 crystiamkellepereiraesilva.pdf: 5368190 bytes, checksum: 22e15248de5dbc282e6d4324b03dca64 (MD5) Previous issue date: 2015-03-27 / Existem inúmeros recursos educacionais distribuídos em diferentes repositórios que abordam um conjunto amplo de assuntos e que possuem objetivos educacionais distintos. A escolha adequada desses recursos educacionais é um desafio para os usuários que desejam usá-los para a sua formação intelectual. Nesse contexto surgem os Sistemas de Recomendação para auxiliar os usuários nessa tarefa. Para que seja possível gerar recomendações personalizadas, torna-se importante identificar informações que ajudem a definir o perfil do usuário e auxiliem na identificação de suas necessidades e interesses. O uso constante e cada vez mais intenso de algumas ferramentas tecnológicas faz com que inúmeras informações a respeito do perfil, dos interesses, das preferências, da forma de interação e do comportamento do usuário possam ser identificadas em decorrência da interação espontânea que ocorre nesses sistemas. Esse é o caso, por exemplo, das redes socais. Neste trabalho é apresentada a proposta e o desenvolvimento de uma arquitetura capaz de extrair características do perfil e do contexto educacional dos usuários, através da rede social Facebook e realizar recomendações de recursos educacionais de forma individualizada e personalizada que sejam condizentes com essas características. A solução proposta é apoiada por técnicas de extração de informações e ontologias para a extração, definição e enriquecimento das características e interesses dos usuários. As técnicas de Extração de Informação foram aplicadas aos textos associados às páginas curtidas e compartilhadas por usuários nas suas redes sociais para extrair informação estruturada que possa ser usada no processo de recomendação de recursos educacionais. Já as ontologias foram usadas para buscar interesses relacionados aos temas extraídos. A recomendação é baseada em repositório de objetos de aprendizagem e em repositórios de dados ligados e é realizada dentro das redes sociais, aproveitando o tempo despendido pelos usuários nas mesmas. A avaliação da proposta foi feita a partir do desenvolvimento de um protótipo, três provas de conceito e um estudo de caso. Os resultados da avaliação mostraram a viabilidade e uma aceitação relevante por parte dos usuários no sentido de extrair informações sobre os seus interesses educacionais, geradas automaticamente da rede social Facebook, enriquecê-las, encontrar interesses implícitos e usar essas informações para recomendar recursos educacionais. Foi verificada também a possibilidade da recomendação de pessoas, permitindo a formação de uma rede de interesses em torno de um determinado tema, indicando aos usuários bons parceiros para estudo e pesquisa. / There are several educational resources distributed in different repositories that address to a wide range of subjects and have different educational goals. The proper choice of these educational resources is a challenge for users who want to use them for their intellectual development. In this context, recommendation systems may help users in this task.In order to be able to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her needs and interests. The constant and ever-increasing use of some technological tools allows the identification of different information about profile, interests, preferences, interaction style and user behavior from the spontaneous interaction that occurs in these systems, as, for example, the social networks. This paper presents the proposal and the development of one architecture able to extract users´ profile characteristics and educational context, from the Facebook social network and recommend educational resources in individualized and personalized manner, consistent with these characteristics. The proposed solution is supported by Information Extraction Techniques and ontologies for the extraction, enrichment and definition of user characteristics and interests. The Information Extraction techniques were applied to texts associated with “LIKE” and shared user´s pages on his social networks to extract structured information that can be used in the recommendation process of educational resources, the ontologies were used to search to interests related to extracted subjects. The recommendation process is based on learning objects repositories and linked data repositories and is carried out within social networks, taking advantage of user time spent at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation results show the viability and relevant users´ acceptance in order to extract information about their educational interests, automatically generated from the Facebook social network, enrich these information, find implicit interests and use this information to recommend educational resources. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.