Em comunidades de prática, a aprendizagem ocorre por meio da interação e colaboração constante de seus participantes, o caráter social destes ambientes é fundamental para a construção do conhecimento. Por este motivo, esta dissertação busca compreender a forma como os usuários interagem em comunidades de prática e propõe a estruturação destas informações em torno de um perfil dinâmico de usuário. A aplicação de perfis de usuário neste contexto permite entender o indivíduo, seus interesses e necessidades. A partir da representação dos participantes de comunidades de prática é possível desenvolver sistemas de aprendizagem inteligente, sistemas de recomendação, elementos de gamificação e sistemas de acesso e recuperação de informação personalizados. Estes mecanismos procuram estimular o engajamento dos participantes e promover a aprendizagem colaborativa. A representação das informações neste trabalho será apoiada pelo uso de tecnologias da web semântica e de ontologias para a formalização das relações em comunidades de prática. O desenvolvimento de softwares educacionais baseados na web semântica amplia a capacidade de implementação de novos mecanismos de aprendizagem, contribuindo para a análise das interações e a inferências sobre as informações dos usuários. O uso de ontologias permite a formalização das informações e torna possível a elaboração de uma rede de conhecimento que pode ser processada e consumida por agentes de software, contribuindo para a interoperabilidade do sistema. / In communities of practice, learning is built through constant interaction and collaboration of their participants, the social aspect of these environments is crucial for the knowledge construction. For this reason, this work intends to understand how users interact in communities of practice and proposes a dynamic user profile to structure this information. An user profile applied in this context allows us to understand the person, his interests and needs. The representation of participants in communities of practice allow us to develop intelligent learning systems, recommender systems, gamification elements and systems for personalized access and personalized information retrieval. Those mechanisms intend to stimulate participant engagement to promote collaborative learning. In this work, semantic web technologies and ontologies are used to represent this informations. The development of educational software based on the semantic web expands the capacity to implement new learning mechanisms, contributing to the analysis of the interactions and the inferences about user informations. The use of ontologies allows the formalization of information and enables the elaboration of a knowledge network that can be processed and consumed by software agents, contributing to the system interoperability.

La personnalisation vise à recueillir les intérêts, les préférences, les usages, les contraintes, le contexte, etc. souvent considérés comme faisant partie de ce que l'on appelle ''profil utilisateur'' pour ensuite les intégrer dans un système et les exploiter afin de permettre à l'utilisateur d'accéder rapidement aux informations les plus pertinentes pour lui. Par ailleurs, au sein d'une organisation, différents acteurs sont amenés à prendre des décisions à différents niveaux de responsabilité et ont donc besoin de réaliser des analyses à partir de l'entrepôt de données pour supporter la prise de décision. Ainsi, dans le contexte de cette communauté d'utilisateurs de l'entrepôt de données, la notion de collaboration émerge. Il est alors intéressant de combiner les concepts de personnalisation et de collaboration pour approcher au mieux les besoins des utilisateurs en leur recommandant des analyses en ligne pertinentes. L'objectif de ce mémoire est de proposer une approche collaborative pour l'OLAP, impliquant plusieurs utilisateurs, dirigée par un processus de personnalisation intégré aux systèmes décisionnels afin de pouvoir aider l'utilisateur final dans son processus d'analyse en ligne. Qu'il s'agisse de personnalisation du modèle d'entrepôt, de recommandation de requêtes décisionnelles ou de recommandation de chemins de navigation au sein des cubes de données, l'utilisateur a besoin d'un système décisionnel efficace qui l'aide dans sa démarche d'analyse en ligne. La finalité est de fournir à l'utilisateur des réponses pertinentes proches de ses besoins pour qu'il puisse mieux appréhender ses prises de décision. Nous nous sommes intéressés dans cette thèse à trois problèmes relevant de la prise en compte de l'utilisateur au sein des entrepôts de données et de l'OLAP. Nos contributions s'appuient sur la combinaison de techniques issues de la fouille de données avec les entrepôts et OLAP. Notre première contribution est une approche qui consiste à personnaliser les hiérarchies de dimensions afin d'obtenir des axes d'analyse nouveaux sémantiquement plus riches pouvant aider l'utilisateur à réaliser de nouvelles analyses non prévues par le modèle de l'entrepôt initial. En effet, nous relâchons la contrainte du modèle fixe de l'entrepôt, ce qui permet à l'utilisateur de créer de nouveaux axes d'analyse pertinents en tenant compte à la fois de ses contraintes et des connaissances enfouies dans les données entreposées. Notre approche repose sur une méthode d'apprentissage non-supervisé, le k-means contraint, capable de créer de nouveaux regroupements intéressants des données entreposées pouvant constituer un nouveau niveau de hiérarchie permettant de réaliser de nouvelles requêtes décisionnelles. L'intérêt est alors de pouvoir exploiter ces nouveaux niveaux de hiérarchie pour que les autres utilisateurs appartenant à la même communauté d'utilisateurs puissent en tirer profit, dans l'esprit d'un système collaboratif dans lequel chacun apporte sa pierre à l'édifice. Notre deuxième contribution est une approche interactive pour aider l'utilisateur à formuler de nouvelles requêtes décisionnelles pour construire des cubes OLAP pertinents en s'appuyant sur ses requêtes décisionnelles passées, ce qui lui permet d'anticiper sur ses besoins d'analyse futurs. Cette approche repose sur l'extraction des motifs fréquents à partir d'une charge de requêtes associée à un ou à un ensemble d'utilisateurs appartenant à la même communauté d'acteurs d'une organisation. Notre intuition est que la pertinence d'une requête décisionnelle est fortement corrélée avec la fréquence d'utilisation par l'utilisateur (ou un ensemble d'utilisateurs) des attributs associés à l'ensemble de ses (leurs) requêtes précédentes. Notre approche de formulation de requêtes (...) / The objective of this thesis is to provide a collaborative approach to the OLAP involving several users, led by an integrated personalization process in decision-making systems in order to help the end user in their analysis process. Whether personalizing the warehouse model, recommending decision queries or recommending navigation paths within the data cubes, the user need an efficient decision-making system that assist him. We were interested in three issues falling within data warehouse and OLAP personalization offering three major contributions. Our contributions are based on a combination of datamining techniques with data warehouses and OLAP technology. Our first contribution is an approach about personalizing dimension hierarchies to obtain new analytical axes semantically richer for the user that can help him to realize new analyzes not provided by the original data warehouse model. Indeed, we relax the constraint of the fixed model of the data warehouse which allows the user to create new relevant analysis axes taking into account both his/her constraints and his/her requirements. Our approach is based on an unsupervised learning method, the constrained k-means. Our goal is then to recommend these new hierarchy levels to other users of the same user community, in the spirit of a collaborative system in which each individual brings his contribution. The second contribution is an interactive approach to help the user to formulate new decision queries to build relevant OLAP cubes based on its past decision queries, allowing it to anticipate its future analysis needs. This approach is based on the extraction of frequent itemsets from a query load associated with one or a set of users belonging to the same actors in a community organization. Our intuition is that the relevance of a decision query is strongly correlated to the usage frequency of the corresponding attributes within a given workload of a user (or group of users). Indeed, our approach of decision queries formulation is a collaborative approach because it allows the user to formulate relevant queries, step by step, from the most commonly used attributes by all actors of the user community. Our third contribution is a navigation paths recommendation approach within OLAP cubes. Users are often left to themselves and are not guided in their navigation process. To overcome this problem, we develop a user-centered approach that suggests the user navigation guidance. Indeed, we guide the user to go to the most interesting facts in OLAP cubes telling him the most relevant navigation paths for him. This approach is based on Markov chains that predict the next analysis query from the only current query. This work is part of a collaborative approach because transition probabilities from one query to another in the cuboids lattice (OLAP cube) is calculated by taking into account all analysis queries of all users belonging to the same community. To validate our proposals, we present a support system user-centered decision which comes in two subsystems: (1) content personalization and (2) recommendation of decision queries and navigation paths. We also conducted experiments that showed the effectiveness of our analysis online user centered approaches using quality measures such as recall and precision.

Abstract User experience has become a popular term in research and industry. There has been a great attempt to study and design user experiences. This thesis gives a practical view to user experience studies and methods by reporting test settings and results of the ADAMOS case studies. The goal of the ADAMOS project was to investigate context- and action-sensitive services in terms of how users experience when the system can detect one’s location and actions, and then adjust according to this information. The aim of this thesis is to investigate problems and issues in studying user experiences of mobile services and to find out in which conditions the study of user experience is possible and meaningful. As a contribution this thesis provides practical information for conducting user experience studies and evaluating experiences. The first contribution is a framework (U2E-Frame), which I created and improved iteratively in each test case. The framework is method-independent and it can be used for planning and conducting tests. The second contribution of the thesis is the practical view to all methods that are created, applied, presented and evaluated in this thesis. Especially during this thesis work three novel methods (Mobile Feedback, 3E-Diary and SUE methodology) have been developed and evaluated. The evaluation of the research methods illustrates that the best practice to study user experience is to use several methods together. This enables deeper understanding of user experiences. As the third contribution of this thesis I introduce a proposal of ten user experience heuristics for design and evaluation of user experiences. The aim of these heuristics is to enable designers to understand what meaning user experience has in product design. Developers can use these heuristics for designing and evaluating user experience aspects in product design. This thesis presents the main challenges in user experience research: know what to study (comprehensive user experience), know how to study it (find appropriate methods) and know how to evaluate and design it (user experience heuristics). An answer to the research problem is that it is both possible and meaningful to study user experience when we know user experience targets, and features of the services we want to investigate, and we can use the most appropriate methods, ensure the participant’s commitment to the test and ensure analysing relationships between results collected with different methods.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2015-12-01T13:50:34Z No. of bitstreams: 1 crystiamkellepereiraesilva.pdf: 5368190 bytes, checksum: 22e15248de5dbc282e6d4324b03dca64 (MD5) / Made available in DSpace on 2015-12-01T13:50:34Z (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.

There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics.

No description available.

Dans cette thèse, nous proposons des algorithmes de recommandation sémantique et sociale, qui recommandent un produit pour les utilisateurs qui sont connectés par un réseau de collaboration sociale. Ces algorithmes utilisent deux types d'informations : information sémantique et information sociale .L' information sémantique est basée sur la pertinence sémantique entre les utilisateurs et le produit à recommandé, tandis que l' information sociale est basée sur la position de l'utilisateur et de leur type et de la qualité des connexions entre eux dans le réseau de collaboration . Enfin, nous utilisons l'algorithme de parcoure profondeur (DFS) et l'algorithme de parcoure en largeur (BFS), pour explorer le réseau social.Utilisation de l' information sémantique et l'information sociale , dans le système de recommandation , nous aide à explorer partiellement le réseau social , ce qui nous conduit à réduire la taille des données explorées et de minimiser le temps de recherche dans le réseau.Nous appliquons nos algorithmes sur des données réelles : MovieLens et Amazon , et nous comparons la précision de la performance de nos algorithmes avec les algorithmes de recommandation classiques , comme l'algorithme de filtrage collaborative et l'algorithme hybrideNos résultats montrent un taux de précision satisfaisants , et une performance très significative du temps d'exécution et de la taille des données explorées , par rapport aux autres algorithmes de recommandation classiques .En fait , l'importance de nos algorithmes repose sur le fait que ces algorithmes explorent une très petite partie du graphe , au lieu d'explorer tout le graphe que les méthodes de recherche classiques , et encore donnent une bonne précision par rapport aux autres algorithmes de recommandation classiques . Donc , en minimisant la taille des données recherchées n'influence pas mal la précision des résultats . / 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.

Nous sommes amenés chaque jour à prendre un nombre important de décisions : quel nouveau livre lire ? Quel film regarder ce soir ou où aller ce week-end ? De plus en plus, nous utilisons les ressources en ligne pour nous aider à prendre des décisions. Comme la prise de décision est assistée par le domaine en ligne, l'utilisation de systèmes de recommandation est devenue essentielle dans la vie quotidienne. Dans le même temps, les réseaux sociaux sont devenus une partie indispensable de ce processus ; partout dans le monde on les utilise quotidiennement pour récupérer des données de personne et de sources d'information en qui on a confiance. Quand les internautes passent du temps sur les réseaux sociaux, ils laissent de précieuses informations sur eux-mêmes. Cela a attiré l'attention des chercheurs et les professionnels de nombreux domaines académiques et commerciaux. Comme le domaine de la recommandation est un domaine qui a assisté à des changements de grande ampleur attribuable à des réseaux sociaux, il y a un intérêt évident pour les systèmes de recommandation sociale. Cependant, dans la littérature de ce domaine, nous avons constaté que de nombreux systèmes de recommandation sociale ont été évalués en utilisant des réseaux sociaux spécialisés comme Epinions, Flixter et d'autres types des réseaux sociaux de recommandation, qui tendent à être composées d'utilisateurs, d'articles, de notes et de relations. Ces solutions ne peuvent pas être étendues directement à des réseaux sociaux à usage général (GPSNs) comme Facebook et Twitter, qui sont des réseaux sociaux ouverts où les utilisateurs peuvent réaliser une variété d'actions utiles pour l'aide à la recommandation / We are surrounded by decisions to take, what book to read next? What film to watch this night and in the week-end? As the number of items became tremendous the use of recommendation systems became essential in daily life. At the same time social network become indispensable in people’s daily lives; people from different countries and age groups use them on a daily basis. While people are spending time on social networks, they are leaving valuable information about them attracting researchers’ attention. Recommendation is one domain that has been affected by the social networks widespread; the result is the social recommenders’ studies. However, in the literature we’ve found that most of the social recommenders were evaluated over Epinions, flixter and other type of domains based recommender social networks, which are composed of (users, items, ratings and relations). The proposed solutions can’t be extended directly to General Purpose Social Networks (GPSN) like Facebook and Twitter which are open social networks where users can do a variety of useful actions that can be useful for recommendation, but as they can’t rate items, these information are not possible to be used in recommender systems! Moreover, evaluations are based on the known metrics like MAE, and RMSE. This can’t guarantee the satisfaction of users, neither the good quality of recommendation

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.

Made available in DSpace on 2016-03-22T17:27:17Z (GMT). No. of bitstreams: 1 Ione Roessler.pdf: 228173 bytes, checksum: a551eef04ff092df0954f471b276e7b5 (MD5) Previous issue date: 2006-07-03 / Objectives: evaluate the user profile of the Programa de Treinamento em Avaliação de Serviços, Licenciamento Sanitário e Acreditação (TALSA Multiplicadores), which was held from July 2003 to June 2004 and was directed towards workers of all categories of the Health Sector. The project was made available free of charge on the Internet through the ONA Portal - National Accreditation Organization. Methods: the analysis, using the chi-square test, was based on final performance of the students, considering the following variables: sex, age, organizations to which they belong, profession, state and region. Results: the present project amounted to 1840 people enrolled in 33 virtual communities. The users were predominantly of the female sex, young, health workers and resident at south-west region of the country. Among the participants, 29.7% completed the training. The only conclusion status association is with sex, 32.2% between the women, versus 26.4% among de men. Conclusion: the program presented a clear national coverage, involved all kinds of organizations of the sector and in a fairly distributed manner, as well as all kinds of professionals and technicians of the Health Sector. / Objetivos: Avaliar o perfil do usuário do Programa de Treinamento em Avaliação de Serviços, Licenciamento Sanitário e Acreditação (TALSA Multiplicadores) de Ensino a Distância (EaD), realizado no período de julho de 2003 a junho de 2004, destinado aos trabalhadores de todas as categorias profissionais do Setor Saúde, veiculado pela Internet através do Portal da ONA Organização Nacional de Acreditação, gratuitamente. Métodos: a análise, por intermédio do teste de qui-quadrado, baseou-se no desempenho final dos participantes conforme as seguintes variáveis: sexo, idade, organizações a que pertencem, categoria profissional, estado e região. Resultados: totalizou-se 1840 pessoas matriculadas em 33 comunidades virtuais. Os usuários eram predominantemente do sexo feminino, jovens, trabalhadores da área da saúde e residentes na região sudeste do país. Dentre os participantes, 29,7% concluiu o treinamento. A única variável associada ao status de concluinte foi o sexo, 32,2% entre as mulheres, versus 26,4% entre os homens. Conclusão: o programa apresentou uma clara abrangência nacional, envolveu todo tipo de organizações do setor e de forma bem distribuída, bem como todo tipo de profissionais e técnicos do Setor Saúde.