Data analysis has become a very important area for both companies and researchers as a consequence of the technological developments in recent years. Companies are trying to increase their profit by analyzing the existing data about their customers and making decisions for the future according to the results of these analyses. Parallel to the need of companies, researchers are investigating different methodologies to analyze data more accurately with high performance. Recommender systems are one of the most popular and widespread data analysis tools. A recommender system applies knowledge discovery techniques to the existing data and makes personalized product recommendations during live customer interaction. However, the huge growth of customers and products especially on the internet, poses some challenges for recommender systems, producing high quality recommendations and performing millions of recommendations per second. In order to improve the performance of recommender systems, researchers have proposed many different methods. Singular Value Decomposition (SVD) technique based on dimension reduction is one of these methods which produces high quality recommendations, but has to undergo very expensive matrix calculations. In this thesis, we propose and experimentally validate some contributions to SVD technique which are based on the user and the item categorization. Besides, we adopt tags to classical 2D (User-Item) SVD technique and report the results of experiments. Results are promising to make more accurate and scalable recommender systems.

This thesis examines recommendation dialogue, in the context of dialogue strategy design for conversational recommender systems. The purpose of a recommender system is to produce personalized recommendations of potentially useful items from a large space of possible options. In a conversational recommender system, this task is approached by utilizing natural language recommendation dialogue for detecting user preferences, as well as for providing recommendations. The fundamental idea of a conversational recommender system is that it relies on dialogue sessions to detect, continuously update, and utilize the user's preferences in order to predict potential interest in domain items modeled in a system. Designing the dialogue strategy management is thus one of the most important tasks for such systems. Based on empirical studies as well as design and implementation of conversational recommender systems, a behavior-based dialogue model called bcorn is presented. bcorn is based on three constructs, which are presented in the thesis. It utilizes a user preference modeling framework (preflets) that supports and utilizes natural language dialogue, and allows for descriptive, comparative, and superlative preference statements, in various situations. Another component of bcorn is its message-passing formalism, pcql, which is a notation used when describing preferential and factual statements and requests. bcorn is designed to be a generic recommendation dialogue strategy with conventional, information-providing, and recommendation capabilities, that each describes a natural chunk of a recommender agent's dialogue strategy, modeled in dialogue behavior diagrams that are run in parallel to give rise to coherent, flexible, and effective dialogue in conversational recommender systems. Three empirical studies have been carried out in order to explore the problem space of recommendation dialogue, and to verify the solutions put forward in this work. Study I is a corpus study in the domain of movie recommendations. The result of the study is a characterization of recommendation dialogue, and forms a base for a first prototype implementation of a human-computer recommendation dialogue control strategy. Study II is an end-user evaluation of the acorn system that implements the dialogue control strategy and results in a verification of the effectiveness and usability of the dialogue strategy. There are also implications that influence the refinement of the model that are used in the bcorn dialogue strategy model. Study III is an overhearer evaluation of a functional conversational recommender system called CoreSong, which implements the bcorn model. The result of the study is indicative of the soundness of the behavior-based approach to conversational recommender system design, as well as the informativeness, naturalness, and coherence of the individual bcorn dialogue behaviors. / I denna avhandling undersöks rekommendationsdialog med avseende på utformningen av dialogstrategier f¨or konverserande rekommendationssystem. Syftet med ett rekommendationssystem är att generera personaliserade rekommendationer utifrån potentiellt användbara domänobjekt i stora informationsrymder. I ett konverserande rekommendationssystem angrips detta problem genom att utnyttja naturligt språkk och dialog för att modellera användarpreferenser, liksom för att ge rekommendationer. Grundidén med konverserande rekommendationssystem är att utnyttja dialogsessioner för att upptäcka, uppdatera och utnyttja en användares preferenser för att förutsäga användarens intresse för domänobjekten som modelleras i ett system. Utformningen av dialogstrategihantering är därför en av de viktigaste uppgifterna för sådana system. Baserat på empiriska studier, liksom på utformning och implementering av konverserande rekommendationssystem, presenteras en beteendebaserad dialogmodell som kallas bcorn. bcorns bas utgörs av tre konstruktioner, vilka alla presenteras i denna avhandling. bcorn utnyttjar ett preferensmodelleringsramverk (preflets) som stöder och anv¨ander sig av naturligt språk i dialog och tillåter deskriptiva, komparativa och superlativa preferensuttryck i olika situationer. Den andra komponenten i bcorn är dess interna meddelande-formalism pcql, som är en notation som kan beskriva preferens- och faktiska påståenden och frågor. bcorn är utformat som en generell rekommendationshanteringsstrategi med konventionella, informationsgivande och rekommenderande förmågor, som var och en beskriver naturliga delar av en rekommendationsagents dialogstrategi. Dessa delar modelleras i dialogbeteendediagram som exekveras parallellt för att ge upphov till koherent, flexibel och effektiv dialog i konverserande rekommendationssystem. Tre empiriska studier har utförts för att utforska problemkomplexet som utgör rekommendationsdialog och för att verifiera de lösningar som tagits fram inom ramen för detta arbete. Studie I är en korpusstudie i filmrekommendationsdomänen. Studien resulterar i en karakteristik av rekommendationsdialog, och utgör basen för en första prototyp av dialoghanteringsstrategi för rekommendationsdialog mellan människa och dator. Studie II är en slutanvändarutvärdering av systemet acorn som implementerar denna dialoghanteringsstrategi och resulterar i en verifiering av effektivitet och användbarhet av strategin. Studien resulterar också i implikationer som påverkar utformningen av den modell som används i bcorn. Studie III är en medhörningsutvärdering av det funktionella konverserande rekommendationssystemet CoreSong, som implementerar bcorn-modellen. Resultatet av studien indikerar att det beteendebaserade angreppssättet är funktionellt och att de olika dialogbeteendena i bcorn ger upphov till h¨og informationskvalitet, naturlighet och koherens i rekommendationsdialog.

Em nossa vida diária, são utilizadas identidades digitais (IDDs) para acessar contas de e-mail, bancos e lojas virtuais, locais restritos, computadores compartilhados, e outros. Garantir que apenas usuários autorizados tenham o acesso permitido é um aspecto fundamental no desenvolvimento destas aplicações. Atualmente, os métodos de controle de acesso simples como senhas ou números de identificação pessoal não devem ser considerados suficientemente seguros, já que um impostor pode conseguir estas informações sem o conhecimento do usuário. Ainda, no caso de utilização de dispositivos físicos como cartões de identificação, estes podem ser roubados ou forjados. Para tornar estes sistemas mais confiáveis, técnicas de autenticação de identidades utilizando múltiplas verificações são propostas. A utilização de características biométricas surge como a alternativa mais confiável para tratar este problema, pois são, teoricamente, únicas para cada pessoa. Contudo, algumas características biométricas como a aparência facial podem variar com o tempo, implicando em um grande desafio para os sistemas de reconhecimento facial. Neste trabalho é combinado o acesso tradicional por senha com a análise da face para realizar a autenticação. Um método de aprendizagem supervisionada é apresentado e sua adaptação é baseada na melhora contínua dos modelos faciais, que são representados por misturas de gaussianas. Os resultados experimentais, obtidos sobre um conjunto de teste reduzido, são encorajadores, com 98% de identificação correta dos usuários e custo computacional relativamente baixo. Ainda, a comparação com um método apresentado na literatura indicou vantagens do método proposto quando usado como um pré-selecionador de faces. / In our daily life, we use digital identities (DIDs) to access e-mails, e-banks, e-shops, physical environments, shared computers, and so on. Guarantee that only authorized users are granted access is an important aspect in the development of such applications. Nowadays, the simple access control methods like passwords or personal identification numbers can not be considered secure enough, because an impostor can obtain and use these information without user knowledge. Also, physical devices like ID cards can be stolen. To make these systems more reliable, multimodal DID authentication techniques combining different verification steps are proposed. Biometric features appears as one of the most reliable alternatives to deal with this problem because, theoretically, they are unique for each person. Nevertheless, some biometric features like face appearances may change in time, posing a serious challenge for a face recognition system. In this thesis work, we use the traditional password access combined with human face analysis to perform the authentication task. An intuitive supervised appearance learning method is presented, and its adaptation is based on continuously improving face models represented using the Gaussian mixture modeling approach. The experimental results over a reduced test set show encouraging results, with 98% of the users correctly identified, with a relatively small computational effort. Still, the comparison with a method presented in the literature indicated advantages of the proposed method when used as a pre-selector of faces.

Em nossa vida diária, são utilizadas identidades digitais (IDDs) para acessar contas de e-mail, bancos e lojas virtuais, locais restritos, computadores compartilhados, e outros. Garantir que apenas usuários autorizados tenham o acesso permitido é um aspecto fundamental no desenvolvimento destas aplicações. Atualmente, os métodos de controle de acesso simples como senhas ou números de identificação pessoal não devem ser considerados suficientemente seguros, já que um impostor pode conseguir estas informações sem o conhecimento do usuário. Ainda, no caso de utilização de dispositivos físicos como cartões de identificação, estes podem ser roubados ou forjados. Para tornar estes sistemas mais confiáveis, técnicas de autenticação de identidades utilizando múltiplas verificações são propostas. A utilização de características biométricas surge como a alternativa mais confiável para tratar este problema, pois são, teoricamente, únicas para cada pessoa. Contudo, algumas características biométricas como a aparência facial podem variar com o tempo, implicando em um grande desafio para os sistemas de reconhecimento facial. Neste trabalho é combinado o acesso tradicional por senha com a análise da face para realizar a autenticação. Um método de aprendizagem supervisionada é apresentado e sua adaptação é baseada na melhora contínua dos modelos faciais, que são representados por misturas de gaussianas. Os resultados experimentais, obtidos sobre um conjunto de teste reduzido, são encorajadores, com 98% de identificação correta dos usuários e custo computacional relativamente baixo. Ainda, a comparação com um método apresentado na literatura indicou vantagens do método proposto quando usado como um pré-selecionador de faces. / In our daily life, we use digital identities (DIDs) to access e-mails, e-banks, e-shops, physical environments, shared computers, and so on. Guarantee that only authorized users are granted access is an important aspect in the development of such applications. Nowadays, the simple access control methods like passwords or personal identification numbers can not be considered secure enough, because an impostor can obtain and use these information without user knowledge. Also, physical devices like ID cards can be stolen. To make these systems more reliable, multimodal DID authentication techniques combining different verification steps are proposed. Biometric features appears as one of the most reliable alternatives to deal with this problem because, theoretically, they are unique for each person. Nevertheless, some biometric features like face appearances may change in time, posing a serious challenge for a face recognition system. In this thesis work, we use the traditional password access combined with human face analysis to perform the authentication task. An intuitive supervised appearance learning method is presented, and its adaptation is based on continuously improving face models represented using the Gaussian mixture modeling approach. The experimental results over a reduced test set show encouraging results, with 98% of the users correctly identified, with a relatively small computational effort. Still, the comparison with a method presented in the literature indicated advantages of the proposed method when used as a pre-selector of faces.

Em nossa vida diária, são utilizadas identidades digitais (IDDs) para acessar contas de e-mail, bancos e lojas virtuais, locais restritos, computadores compartilhados, e outros. Garantir que apenas usuários autorizados tenham o acesso permitido é um aspecto fundamental no desenvolvimento destas aplicações. Atualmente, os métodos de controle de acesso simples como senhas ou números de identificação pessoal não devem ser considerados suficientemente seguros, já que um impostor pode conseguir estas informações sem o conhecimento do usuário. Ainda, no caso de utilização de dispositivos físicos como cartões de identificação, estes podem ser roubados ou forjados. Para tornar estes sistemas mais confiáveis, técnicas de autenticação de identidades utilizando múltiplas verificações são propostas. A utilização de características biométricas surge como a alternativa mais confiável para tratar este problema, pois são, teoricamente, únicas para cada pessoa. Contudo, algumas características biométricas como a aparência facial podem variar com o tempo, implicando em um grande desafio para os sistemas de reconhecimento facial. Neste trabalho é combinado o acesso tradicional por senha com a análise da face para realizar a autenticação. Um método de aprendizagem supervisionada é apresentado e sua adaptação é baseada na melhora contínua dos modelos faciais, que são representados por misturas de gaussianas. Os resultados experimentais, obtidos sobre um conjunto de teste reduzido, são encorajadores, com 98% de identificação correta dos usuários e custo computacional relativamente baixo. Ainda, a comparação com um método apresentado na literatura indicou vantagens do método proposto quando usado como um pré-selecionador de faces. / In our daily life, we use digital identities (DIDs) to access e-mails, e-banks, e-shops, physical environments, shared computers, and so on. Guarantee that only authorized users are granted access is an important aspect in the development of such applications. Nowadays, the simple access control methods like passwords or personal identification numbers can not be considered secure enough, because an impostor can obtain and use these information without user knowledge. Also, physical devices like ID cards can be stolen. To make these systems more reliable, multimodal DID authentication techniques combining different verification steps are proposed. Biometric features appears as one of the most reliable alternatives to deal with this problem because, theoretically, they are unique for each person. Nevertheless, some biometric features like face appearances may change in time, posing a serious challenge for a face recognition system. In this thesis work, we use the traditional password access combined with human face analysis to perform the authentication task. An intuitive supervised appearance learning method is presented, and its adaptation is based on continuously improving face models represented using the Gaussian mixture modeling approach. The experimental results over a reduced test set show encouraging results, with 98% of the users correctly identified, with a relatively small computational effort. Still, the comparison with a method presented in the literature indicated advantages of the proposed method when used as a pre-selector of faces.

Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data. Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing system capable of achieving tasks practically impossible for conventional sensing platforms. One of the advantages of participatory sensing platforms is their mobility, since human users are often in motion. This dissertation presents a set of techniques for modeling and predicting user transportation patterns from cell-phone and social media check-ins. To study large-scale transportation patterns, I created a mobile phone app, Kpark, for estimating parking lot occupancy on the UCF campus. Kpark aggregates individual user reports on parking space availability to produce a global picture across all the campus lots using crowdsourcing. An issue with crowdsourcing is the possibility of receiving inaccurate information from users, either through error or malicious motivations. One method of combating this problem is to model the trustworthiness of individual participants to use that information to selectively include or discard data. This dissertation presents a comprehensive study of the performance of different worker quality and data fusion models with plausible simulated user populations, as well as an evaluation of their performance on the real data obtained from a full release of the Kpark app on the UCF Orlando campus. To evaluate individual trust prediction methods, an algorithm selection portfolio was introduced to take advantage of the strengths of each method and maximize the overall prediction performance. Like many other crowdsourced applications, user incentivization is an important aspect of creating a successful crowdsourcing workflow. For this project a form of non-monetized incentivization called gamification was used in order to create competition among users with the aim of increasing the quantity and quality of data submitted to the project. This dissertation reports on the performance of Kpark at predicting parking occupancy, increasing user app usage, and predicting worker quality.

En intégrant le cycle de vie d'un bien dans son processus de développement, l'éco-conception permet d'améliorer la performance environnementale de ce futur produit. La combinaison, dans cette thèse, des notions de la conception centrée utilisateur et de la pensée cycle de vie, permet de proposer une méthode pour l'intégration de la phase d'utilisation en éco-conception. La méthode repose sur la proposition de nouveaux modèles pour représenter l'utilisation adaptés aux spécificités de l'éco-conception. A partir du modèle de produit, un modèle de la phase d'utilisation, connectée aux phases de distribution et de fin de vie, permet d'associer l'utilisation du produit à son cycle de vie complet. Le modèle de contexte permet de positionner la performance environnementale du produit en utilisation par rapport à des paramètres associés à l'utilisateur et à l'environnement d'utilisation. L'évaluation environnementale du scénario d'utilisation regroupant tous ces modèles permet d'adapter les stratégies d'amélioration de la méthode à l'utilisation d'un produit en conception. Une application de la démarche à la re-conception d'un réfrigérateur pour la France et le Brésil permet d'illustrer le fonctionnement de la méthode. / By integrating product life cycle into the developement process, ecodesign enables the environnemental improvement of the product to be. The combination of user centred design proposition and life cycle thinking allows us to propose a method for use phase integration in ecodesign. This method is built on new models to represent product use that are compatible with ecodesign specificity. Based on the product model, the use phase model, which is connected to the distribution and end-of-life phases, facilitates the attachment of product use to the complete life cycle. Context model aims at possitionning the product use environmental performance according to influencing parameters related to the user and it environment. Environmental assessment is made based on the use scenario, a combination of the three previous models. This assessment enables to adapt the improvements strategies to the specificity of a product use phase. A case study has been perform to illustrate the capability of the new proposal to ecodesign a refrigerator for Brazil and for France.

O advento da TV Digital Interativa no mundo modifica, em definitivo, a experiência do usuário em assistir a TV, tornando-a mais rica principalmente pelo uso do recurso da interatividade. Os usuários passam a ser pró-ativos e começam a interagir das mais diversas formas: construção de comunidades virtuais, discussão sobre um determinado conteúdo, envio de mensagens e recomendações, dentre outras. Neste cenário a participação dos usuários de forma colaborativa assume um papel importante e essencial. Aliado a isso, a recepção na TV Digital Interativa é feita através de dispositivos computacionais que, devido à convergência digital, estão presentes cada vez mais em meios ubíquos. Um outro fator preponderante a considerar, resultante desta mídia, corresponde ao crescimento da quantidade e diversidade de programas e serviços interativos disponíveis, dificultando, assim, a seleção de conteúdo de maior relevância. Diante dos fatos expostos, esta pesquisa tem como principal objetivo propor e implementar uma infra-estrutura de software no cenário da TV Digital Interativa intitulada CollaboraTVware para orientar, de forma transparente, os usuários na escolha de programas e serviços interativos através da participação colaborativa de outros usuários com perfis e contextos similares. No escopo deste trabalho, a participação colaborativa corresponde às avaliações atribuídas por usuários no sentido de expressar opiniões sobre os conteúdos veiculados. As modelagens de usuário, do dispositivo utilizado e do contexto da interação do usuário, essenciais para o desenvolvimento do CollaboraTVware, são representadas por padrões de metadados flexíveis usados no domínio da TV Digital Interativa (MPEG-7, MPEG-21 e TV-Anytime), e suas devidas extensões. A arquitetura do CollaboraTVware é composta por dois subsistemas: dispositivo do usuário e provedor de serviços. A tarefa de classificação, da teoria de mineração de dados, é a abordagem adotada na concepção da infra-estrutura. O conceito de perfil de uso participativo é apresentado e discutido. Para demonstrar e validar as funcionalidades do CollaboraTVware em um cenário de uso, foi desenvolvida uma aplicação (EPG colaborativo) como estudo de caso. / The advent of the Interactive Digital TV around the world transforms, ultimately, the user experience in watching TV, making it richer mainly by enabling user interactivity. The users become pro-active and begin to interact with very different ways: building virtual communities, discussion about contents, sending messages and recommendations etc. In this scenario the user participation in a collaborative assumes an important and essential role. Additionally, the reception in Interactive Digital TV is done by devices that due to digital convergence are increasingly present in ubiquitous environments. Another preponderant issue to consider, resulting from this media, is the growing of the number and diversity of programs and interactive services available, increasing the difficulty of selecting relevant content. Thus, the main objective of this work is to propose and implement a software infrastructure in an Interactive Digital Television environment entitled CollaboraTVware to guide in a transparent way, users in the choice of programs and interactive services through the collaborative participation of other users with similar profiles and contexts. In the scope of this work, the collaborative participation corresponds to the rating given by users in order to express opinions about the content transmitted. The modeling of user, device used and context of user interaction, essential for the development of CollaboraTVware, are represented by granular metadata standards used in the field of Interactive Digital TV (MPEG-7, MPEG-21 and TV-Anytime), and its extensions needed. The CollaboraTVware architecture is composed of two subsystems: user device and service provider. The classification task, from the theory of data mining, is the approach adopted in the infrastructure design. The concept of participative usage profile is presented and discussed. To demonstrate the functionalities in a use scenario, was developed an application (collaborative EPG) as a case study which uses the CollaboraTVware.

Un système de recommandation a pour objectif de recommander à un utilisateur, appelé utilisateur actif, des ressources pertinentes pour lui. Le filtrage collaboratif (FC) est une approche de recommandation très répandue qui exploite les préférences exprimées par des utilisateurs sur des ressources. Le FC repose sur l'hypothèse que les préférences des utilisateurs sont cohérentes entre elles, ce qui permet d'inférer les préférences d'un utilisateur à partir des préférences des autres utilisateurs. Définissons une préférence spécifique comme une préférence qui ne serait partagée pour aucun groupe d'utilisateurs. Un utilisateur possédant plusieurs préférences spécifiques qu'il ne partage avec aucun autre utilisateur sera probablement mal servi par une approche de FC classique. Il s'agit du problème des Grey Sheep Users (GSU). Dans cette thèse, je réponds à trois questions distinctes. 1) Qu'est-ce qu'une préférence spécifique ? J'apporte une réponse en proposant des hypothèses associées que je valide expérimentalement. 2) Comment identifier les GSU dans les données ? Cette identification est importante afin d'anticiper les mauvaises recommandations qui seront fournies à ces utilisateurs. Je propose des mesures numériques permettant d'identifier les GSU dans un jeu de données de recommandation sociale. Ces mesures sont significativement plus performantes que celles de l'état de l'art. Enfin, comment modéliser ces GSU pour améliorer la qualité des recommandations qui leurs sont fournies ? Je propose des méthodes inspirées du domaine de l'apprentissage automatique et dédiées à la modélisation des GSU permettant d'améliorer la qualité des recommandations qui leurs sont fournies / A recommender system aims at providing relevant resources to a user, named the active user. To allow this recommendation, the system exploits the information it has collected about the active user or about resources. The collaborative filtering (CF) is a widely used recommandation approach. The data exploited by CF are the preferences expressed by users on resources. CF is based on the assumption that preferences are consistent between users, allowing a user's preferences to be inferred from the preferences of other users. In a CF-based recommender system, at least one user community has to share the preferences of the active user to provide him with high quality recommendations. Let us define a specific preference as a preference that is not shared by any group of user. A user with several specific preferences will likely be poorly served by a classic CF approach. This is the problem of Grey Sheep Users (GSU). In this thesis, I focus on three separate questions. 1) What is a specific preference? I give an answer by proposing associated hypotheses that I validate experimentally. 2) How to identify GSU in preference data? This identification is important to anticipate the low quality recommendations that will be provided to these users. I propose numerical indicators to identify GSU in a social recommendation dataset. These indicators outperform those of the state of the art and allow to isolate users whose quality of recommendations is very low. 3) How can I model GSU to improve the quality of the recommendations they receive? I propose new recommendation approaches to allow GSU to benefit from the opinions of other users

Submitted by William Justo Figueiro (williamjf) on 2015-08-28T17:26:34Z No. of bitstreams: 1 34e.pdf: 3564229 bytes, checksum: 47f535efb73877017d3fc9b78250f707 (MD5) / Made available in DSpace on 2015-08-28T17:26:34Z (GMT). No. of bitstreams: 1 34e.pdf: 3564229 bytes, checksum: 47f535efb73877017d3fc9b78250f707 (MD5) Previous issue date: 2013 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Um dos principais desafios de sistemas ubíquos e sensíveis a contexto é a coleta de informações relevantes sobre entidades, e o uso destas informações para compreender e prever seu comportamento. Isto permite que as aplicações adaptem-se às entidades, evitando assim uma sobrecarga de questionamentos e informações à entidade. Este trabalho apresenta o eProfile, um modelo que permite que aplicações registrem as ações de entidades em trilhas e infiram informações de perfil a partir destas trilhas, utilizando interoperabilidade semântica e assim permitindo que diferentes aplicações compartilhem informações em um perfil unificado. Foi desenvolvido um protótipo para a avaliação do modelo, o qual foi integrado com dois diferentes softwares. Foi verificado que é possível enriquecer a geração de perfis de aplicações através da integração com o modelo. As contribuições deste modelo são o uso de trilhas para extrair perfis, a geração de perfis dinâmicos, o gerenciamento de regras de inferência e modelos de entidades dinâmicos e a interoperabilidade semântica do modelo. / Context-aware and ubiquitous systems have the challenge of implicitly collect relevant information about entities, and use this information to understand and predict their behaviour. This allows the applications to adapt themselves to the entities, thus avoiding to overflow them with inquires and information. The analysis of trails, the context-aware history of actions, can further improve the relevance of information. This dissertation proposes a model that allows applications to register entites’ actions in trails and infer profile information from these trails, using semantic interoperability and thus allowing different applications to share information and infer a unified profile. A prototype was developed for the evaluation of the model, and it was integrated with two different softwares. It was verified that was possible to enrich the profile generation of applications through the integration with the modelo. The contributions of this model are the use of trails for extracting profiles, the generation of dynamic profiles, the capability of managing dynamic inference rules for profile generation and the semantic interoperabilty of the model.