Σκοπός την εργασίας είναι η δημιουργία ενός ολοκληρωμένου ηλεκτρονικού καταστήματος το οποίο θα παρέχει στους χρήστες μεταξύ άλλων, δυνατότητα χωρικής αναζήτησης προϊόντων, και προσωποποίησης. Η εφαρμογή υποστηρίζει όλες τις λειτουργίες ενός σύγχρονου ηλεκτρονικού καταστήματος προσθέτοντας σε αυτές ένα πλήθος καινοτόμων λειτουργιών. Συγκεκριμένα υποστηρίζει λειτουργία καλαθιού αγορών και παραγγελιών μέσω πιστωτικής κάρτας χρησιμοποιώντας ένα εικονικό σύστημα τραπεζικών συναλλαγών, πλήθος λειτουργιών αναζήτησης προϊόντων, διαφορετικά είδη προσωποποίησης, πολλαπλά επίπεδα ασφάλειας με χρήση κρυπτογράφησης, δημιουργία λογαριασμών χρηστών, forum κα. Στις καινοτομίες της εφαρμογής συγκαταλέγονται η χωρική αναζήτηση προϊόντων με χρήση Τ.Κ., η αναζήτηση αντίστοιχων τιμών σε άλλα καταστήματα και η social προσωποποίηση με χρήση βαρών στους παράγοντες προσωποποίησης. Ιδιαίτερη βαρύτητα έχει δοθεί επίσης στην συντηρησιμότητα του συστήματος και στη φιλικότητα προς τον χρήστη. Οι τεχνολογίες που χρησιμοποιήθηκαν είναι κατά κύριο λόγο HTML, CSS, PHP, Postgresql, smarty και λειτουργικό σύστημα Linux. / The purpose of this thesis is the development of a comprehensive online store that supports all functions of a modern e-shop plus a host of innovative features. Specifically, the application supports shopping cart, orders pipeline using a virtual banking system, many search products features, different kinds of personalization, multiple levels of security using encryption, user accounts, forum etc.. Innovations in the application include spatial search for products using postal codes, search for corresponding values in other e-shops and social personalization using weights to the personalization factors. Special attention was also given to the maintainability of the system and user friendliness. For the implementation of the application were used HTML, CSS, PHP, Postgresql, smarty and Linux operating system.

No description available.

As melhorias alcançadas na Educação através do uso das tecnologias de informação e comunicação são inegáveis. Entretanto, os avanços em direção a um ensino personalizado e direcionado às preferências, ao nivelamento (saneamento das dificuldades e pré-requisitos de conteúdo) e aos interesses dos estudantes são pequenos. Desta forma, através da identificação dos conhecimentos prévios dos alunos, bem como dos conteúdos (conceituais) buscou-se aprimorar o processo de ensino para que cada aluno alcance, a seu modo (de forma personalizada) o aprendizado e possa avançar na disciplina/ano em questão. Como é humanamente impossível um professor conhecer as características e dificuldades de conteúdo de todos os seus alunos, é interessante que o ambiente virtual que apoia o professor possa auxiliar para um atendimento mais personalizado, permitindo que o aluno interaja no seu próprio ritmo de estudo, acesse os conteúdos necessários ou aprofunde-se em algum conteúdo que ele já domine. Esta tese propõe um método de uso das ferramentas disponíveis de ambientes virtuais, principalmente do MOODLE, para alcançar a personalização do ensino e conduzir o aluno em sua própria trajetória individual rumo ao aprendizado significativo, sanando dificuldades de conteúdo. Seguindo o objetivo de melhorar a qualidade do ensino sem necessidade de grade investimento de desenvolvimento de ambiente nem aporte financeiro. Para validar o método foi realizado um projeto piloto mais dois experimentos, todos no Colégio Militar de Porto Alegre que totalizaram a participação de 256 alunos. Os resultados demonstraram a viabilidade do método, medido através da melhora nas notas (análise quantitativa) e da análise qualitativa dos dados e dos questionários (respondidos pelos alunos e professores). Destaca-se como vantagem educacional o desenvolvimento do método que pode ser aplicado a qualquer disciplina ou modelo de ensino e como vantagem computacional o uso do MOODLE para personalizar o ensino. / The use of information and communication technologies made undeniable advances in education. However, advances towards personalized learning and directed to the preferences, the leveling (improvement of difficulties and prerequisites) and to the interests of students are small yet. Thus, it is intended, by identifying the students' prior knowledge and content (conceptual) to improve the process so that each student reach, at their mode (personalized) the learning and make progress in discipline/year in question. We know that is humanly impossible for a teacher to know the characteristics and difficulties of content of all his students. So, the virtual environment that supports the teacher should have the flexibility to customize interactions with each student, which allows the student to interact at his/her own study pace, access the necessary content or deepen in some content that he/she has mastered. This thesis proposes a method for using the available virtual environments tools, especially the MOODLE, to achieve the personalization of teaching and to lead the student in his own individual trajectory toward meaningful learning, healing content difficulties. Following the objective of improving the quality of education without the need for large investment in environmental development or financial contribution. To validate the method was carried out a pilot project plus two experiments, all occurred at Military College of Porto Alegre, with a total of 256 students. The results showed the viability of the method, measured by the improvement in grades (quantitative analysis) and the qualitative analysis of data and questionnaires (answered by students and teachers). It is highlighted as an educational advantage, the development of the method that can be applied to any discipline or teaching model and as computational advantage, the use of MOODLE to customize teaching.

Esta dissertação tem como objetivo identificar de que forma a revista Bravo! (1997-2013) acionou o sujeito perito na capa, espaço emblemático de consagração. Compreendemos o jornalismo como sendo capaz de estabelecer hierarquias, conceder visibilidade e prestígio, estabelecendo, assim, o que deve ser lido, visto e assistido, ou seja, o que é relevante, de acordo com seus critérios. O jornalismo cultural, enquanto especialidade jornalística, reafirma também seu lugar de suposto sistema perito, a partir da confiança do leitor, silenciando ou consolidando valores culturais a partir de seus locais de maior visibilidade, como as capas de revista. Entendendo esse espaço como um lugar consagrado e consagrante, voltamos nosso olhar a quem nele se destaca, sujeitos que resguardam uma suposta excelência técnica e valores canônicos. Com o aporte da Análise de Conteúdo (AC), realizamos um mapeamento das 192 edições da revista, verificando o conteúdo das capas a partir da definição de segmento (música, cinema, literatura, artes visuais, teatro e dança), pessoas contempladas na primeira página, temática e gancho temporal jornalístico, o que resultou em uma panorâmica editorial sobre os principais temas e sujeitos abordados. A partir dessa primeira visada, delimitamos o corpus de 124 capas em que constam somente pessoas. A análise panorâmica mostrou que música foi o segmento mais personalizado, seguido por livros, cinema, teatro, artes visuais e dança. Além disso, percebemos que a lógica do evento, gancho jornalístico de maior incidência, tende a se vincular com pessoas que já possuem prestígio em seu campo de atuação. Junto à descrição quantitativa por segmento cultural, partimos para a leitura qualitativa em profundidade de onze capas que representam tendências do conjunto. Os resultados indicam que, enquanto instância de legitimação desses peritos, Bravo! se utilizou de critérios editoriais específicos para escolher a personalidade de capa, agregando para si os valores associados de quem elegeu, seguindo a lógica da personalização. Ao criar um espaço consagratório em sua primeira página, a revista reafirma a encenação do superlativo, que articula a imagem do rosto, as chamadas, o título e o nome da publicação, como uma forma de acionamento do perito. / The aim of this thesis is to identify how Bravo! (1997-2013) operated the expert in its cover, an emblematic space of consecration. We understand journalism as being able to establish hierarchies, provide visibility and prestige, establishing what should be read, seen and watched, that is, what is important according to its criteria. Cultural journalism, as journalistic specialty, also claims its place of supposed expert system, based on the reader’s confidence, silencing or consolidating cultural values from their places of higher visibility, as magazine covers. Understanding this place as a consecrated and consecrating space, we turn our gaze to whom it stands out, people who keep a supposed technical excellence and canonical values. Through Content Analysis (CA) theory, we conducted a mapping of 192 editions of the magazine by checking the content of the covers from the segment definition (music, cinema, literature, visual arts, theater and dance), people featured on the first page, theme and journalistic temporal hook, which resulted in an editorial overview of the main themes and people covered. From this first view, we delimited the corpus to 124 covers that included only people. The panoramic analysis showed that music was the most personalized segment, followed by literature, cinema, theater, visual arts and dance. Also, we realized that the logic of the event, journalistic hook with the higher incidence, tends to bond with people who are already prestigious in their field. Next to the quantitative description by cultural segment, we made a qualitative in-depth reading of eleven covers that represent set group trends. The results indicate that, while instance of legitimation, Bravo! employed specific editorial criteria to choose the person of the cover, adding itself the associated values of those elected, following the logic of personalization. By creating a consecrational space on its front page, the magazine reaffirms the staging of superlative, that articulates the image of the face, the headlines, the title and the publication name as a form to operate the expert.

A pressing need for efficient personalized recommendations has emerged in crowdsourcing systems. On the one hand, workers confront a flood of tasks, and they often spend too much time to find tasks matching their skills and interests. Thus, workers want effective recommendation of the most suitable tasks with regard to their skills and preferences. On the other hand, requesters sometimes receive results in low-quality completion since a less qualified worker may start working on a task before a better-skilled worker may get hands on. Thus, requesters want reliable recommendation of the best workers for their tasks in terms of workers' qualifications and accountability. The task and worker recommendation problems in crowdsourcing systems have brought up unique characteristics that are not present in traditional recommendation scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers' capabilities, and the quality of the completed tasks. These unique features make traditional recommendation approaches (mostly developed for e-commerce markets) no longer satisfactory for task and worker recommendation in crowdsourcing systems. In this research, we reveal our insight into the essential difference between the tasks in crowdsourcing systems and the products/items in e-commerce markets, and the difference between buyers' interests in products/items and workers' interests in tasks. Our insight inspires us to bring up categories as a key mediation mechanism between workers and tasks. We propose a two-tier data representation scheme (defining a worker-category suitability score and a worker-task attractiveness score) to support personalized task and worker recommendation. We also extend two optimization methods, namely least mean square error (LMS) and Bayesian personalized rank (BPR) in order to better fit the characteristics of task/worker recommendation in crowdsourcing systems. We then integrate the proposed representation scheme and the extended optimization methods along with the two adapted popular learning models, i.e., matrix factorization and kNN, and result in two lines of top-N recommendation algorithms for crowdsourcing systems: (1) Top-N-Tasks (TNT) recommendation algorithms for discovering the top-N most suitable tasks for a given worker, and (2) Top-N-Workers (TNW) recommendation algorithms for identifying the top-N best workers for a task requester. An extensive experimental study is conducted that validates the effectiveness and efficiency of a broad spectrum of algorithms, accompanied by our analysis and the insights gained.

Purpose – The aim of this paper is to create an understanding of what types of initial contacts exist within the service encounter and their functions for the interaction. Design/methodology/approach – To study the initial contact between the employee and the customer, we have used a qualitative approach where observations were the data collecting method. The observations have been conducted in Karlstad at 22 different locations, which generated a total of 83 observations. These observations were analyzed and generated concepts and categories, that were incorporated into themes. Findings – The research showed five types of initial contacts could be identified along with their functions. The types identified are the time effective, the improvised, the committed, the observant and the strategic positioning. Originality/value – By narrowing the interaction to the first moment, this paper contributes knowledge about the initial contact which will facilitate frontline employees work in the face-to-face interaction with customers. Research limitation/implications for future research – The study is conducted in Karlstad. Depending on which kind of industry examined and contextual factors, variations in the result may occur. The findings could differ if cultural perspectives were considered, which could be something to include in future research. Only the face-to-face interaction is examined. This creates opportunities for future research to extend this study by, for instance, investigate the initial contact over internet or phone.

O fenômeno global de envelhecimento populacional está sendo acompanhado pelo rápido crescimento do número de pessoas com algum tipo de deficiência mental ou física, assim como diversas doenças crônicas relacionadas à idade. Devido ao fato de que a maioria dos idosos prefere ficar no conforto de suas residências, é essencial o desenvolvimento de tecnologias que auxiliem os idosos a terem uma vida independente e confortável em seus lares. Em especial, Ambientes Assistidos buscam agir proativamente de forma a auxilar de maneira correta o usuário na execução das suas Atividades de Vida Diária. Devido à grande variabilidade nos níveis de deficiência dos usuários, é fundamental que o Ambiente Assistido seja capaz de fornecer a assistência levando em consideração diversos aspectos relacionados ao usuário, como suas dificuldades, limitações e atividade sendo realizada. Este trabalho propõe um sistema que utiliza uma abordagem baseada nas tecnologias da Web Semântica e no paradigma multiagentes com o objetivo de fornecer assistência personalizada a pessoas idosas e com diversidades funcionais. As características do usuário foram modeladas por uma ontologia OWL, enquanto o mecanismo de reasoning responsável por inferir as preferências relacionadas aos serviços do ambiente foi implementada utilizando a linguagem SWRL. Ainda, os componentes presentes na arquitetura proposta são implementados na forma de agentes com funções específicas. Entre as característica inovadoras da proposta estão a incorporação de um modelo padronizado e internacionalmente conhecido para representar as deficiências, dificuldades e limitações dos usuários e a utilização de uma arquitetura distribuída que possui as características inerentes ao paradigma multiagentes, tais como escalabilidade, flexibilidade e dinamismo. A validação do sistema foi realizada através de sua integração com uma Arquitetura Orientada a Serviços que utiliza uma solução comercial de automação residencial e um simulador de Ambientes Inteligentes, e da sua utilização em três casos de usos definidos pelo projeto AAL Joint Programme. / The global phenomenon of population aging has been followed by the fast growth in the number of people with some kind of mental or physical disability and chronic illnesses associated with old age. Given the fact that the majority of older adults prefer to stay in the comfort of their own homes, it is crucial to develop technologies that help the elderly to live independently and comfortably in their homes. Specifically, Ambient Assisted Living environments aims to act proactively in order to properly assist the elderly and people with functional diversity to perform their Activities of Daily Living. Since these users tend to be greatly variable in regards to functioning and disability levels, it is essential that the ambient provides personalized assistance based on the user’s unique requirements, limitations, preferences, and activity being performed. This work proposes a system that uses an approach based on the Web Semantic technologies and the multiagent paradigm with the goal of providing personalized assistance to the elderly and people with functional diversity. The user’s characteristics were modeled using an OWL ontology and a reasoning mechanism responsible to infer the preferences related to the ambient services was implemented using SWRL rules. Furthermore, the proposed architecture components were implemented as agents with specific roles. Among the proposed system innovative features are the incorporation of a standardized and international model used to represent the user’s functional and disability information, and the use of a distributed architecture having the multiagent paradigm features, such as scalability, flexibility, and dynamism. The system’s validation was accomplished by its integration with a Service Oriented Architecture that uses a commercial automation solution and a smart environment simulator, and by its use in three use cases developed by the AAL Joint Programme.

As melhorias alcançadas na Educação através do uso das tecnologias de informação e comunicação são inegáveis. Entretanto, os avanços em direção a um ensino personalizado e direcionado às preferências, ao nivelamento (saneamento das dificuldades e pré-requisitos de conteúdo) e aos interesses dos estudantes são pequenos. Desta forma, através da identificação dos conhecimentos prévios dos alunos, bem como dos conteúdos (conceituais) buscou-se aprimorar o processo de ensino para que cada aluno alcance, a seu modo (de forma personalizada) o aprendizado e possa avançar na disciplina/ano em questão. Como é humanamente impossível um professor conhecer as características e dificuldades de conteúdo de todos os seus alunos, é interessante que o ambiente virtual que apoia o professor possa auxiliar para um atendimento mais personalizado, permitindo que o aluno interaja no seu próprio ritmo de estudo, acesse os conteúdos necessários ou aprofunde-se em algum conteúdo que ele já domine. Esta tese propõe um método de uso das ferramentas disponíveis de ambientes virtuais, principalmente do MOODLE, para alcançar a personalização do ensino e conduzir o aluno em sua própria trajetória individual rumo ao aprendizado significativo, sanando dificuldades de conteúdo. Seguindo o objetivo de melhorar a qualidade do ensino sem necessidade de grade investimento de desenvolvimento de ambiente nem aporte financeiro. Para validar o método foi realizado um projeto piloto mais dois experimentos, todos no Colégio Militar de Porto Alegre que totalizaram a participação de 256 alunos. Os resultados demonstraram a viabilidade do método, medido através da melhora nas notas (análise quantitativa) e da análise qualitativa dos dados e dos questionários (respondidos pelos alunos e professores). Destaca-se como vantagem educacional o desenvolvimento do método que pode ser aplicado a qualquer disciplina ou modelo de ensino e como vantagem computacional o uso do MOODLE para personalizar o ensino. / The use of information and communication technologies made undeniable advances in education. However, advances towards personalized learning and directed to the preferences, the leveling (improvement of difficulties and prerequisites) and to the interests of students are small yet. Thus, it is intended, by identifying the students' prior knowledge and content (conceptual) to improve the process so that each student reach, at their mode (personalized) the learning and make progress in discipline/year in question. We know that is humanly impossible for a teacher to know the characteristics and difficulties of content of all his students. So, the virtual environment that supports the teacher should have the flexibility to customize interactions with each student, which allows the student to interact at his/her own study pace, access the necessary content or deepen in some content that he/she has mastered. This thesis proposes a method for using the available virtual environments tools, especially the MOODLE, to achieve the personalization of teaching and to lead the student in his own individual trajectory toward meaningful learning, healing content difficulties. Following the objective of improving the quality of education without the need for large investment in environmental development or financial contribution. To validate the method was carried out a pilot project plus two experiments, all occurred at Military College of Porto Alegre, with a total of 256 students. The results showed the viability of the method, measured by the improvement in grades (quantitative analysis) and the qualitative analysis of data and questionnaires (answered by students and teachers). It is highlighted as an educational advantage, the development of the method that can be applied to any discipline or teaching model and as computational advantage, the use of MOODLE to customize teaching.

L'apprentissage de geste est un processus complexe, impliquant de nombreux processus sous-jacents (psychomoteurs, cognitifs, biophysiques). Cet apprentissage peut être divisé en plusieurs grandes phases, définies par la capacité de l'apprenant à produire et à percevoir son geste. A l'instar d'un formateur adaptant son discours et son attitude à l'apprenant, un environnement d'apprentissage doit pouvoir adapter les aides qu'il fournit, d'une part en analysant l'état de l'apprenant, et d'autre part en prédisant les aides qui bénéficieront à son apprentissage. Nous proposons une approche modélisant les interactions entre l'apprenant et l'environnement d'apprentissage; c'est-à-dire l'évolution de l'état de l'apprenant en fonction des configurations successives de l'environnement, permettant une adaptation automatique et dynamique de la sélection d'aides. En s'appuyant sur des travaux portant sur la reconnaissance de gestes et les tuteurs intelligents, nous proposons un cadre formel permettant de représenter la qualité de l'activité gestuelle dans un espace métrique. Cette représentation interprète les résultats issus de modèles probabilistes comme des variables floues illustrant le niveau de l'apprenant sur chacune des dimensions du geste. Nous représentons l'environnement comme l'ensemble des actions possibles, chaque action étant représentée par un vecteur de paramètres estimant la situation dans laquelle l'action est la plus pertinente. Dans un premier temps, ces paramètres sont fixés par des experts, et nous considérons cette expertise comme parfaite. Dans un second temps, nous étudions une problématique omniprésente dans les domaines des environnements d'apprentissage, qui fait état de l'écart entre l’expertise et la réalité. Les domaines "mal définis" sont en effet répandus, du fait du coût et de la difficulté à réunir des experts, et de la complexité inhérente à la définition précise d'un domaine. Ne considérant plus le seul avis des experts, le processus de sélection d 'aides peut alors être vu comme une séquence de décisions dont l'objectif est de proposer à chaque itération l'action qui maximisera le gain en matière d'apprentissage. En s'appuyant sur des travaux récents portant sur les séquences de décisions, notre approche considère le processus de sélection d'aides comme un problème de bandit. Le problème de bandit vise à maximiser un gain lors d'une séquence de décisions, et modélise le compromis entre exploration (choisir une action dont l'influence est inconnue), et exploitation (choisir la meilleure action connue). Nous proposons dans ce cadre une extension des méthodes SoftMax. L'implémentation de notre modèle sur une plateforme d'apprentissage de la calligraphie a été réalisée dans le cadre d'une collaboration avec des experts de ce domaine. Nous montrons, au travers de deux cas d’étude, l'intérêt de notre modèle pour l'apprentissage de la calligraphie. Dans le premier cas d’étude, l'adaptation est construite depuis notre représentation du geste, et un ensemble de règles fixées par des experts. Nous y montrons l'avantage apporté par l'apport d'une diversité d'aides. Dans le second cas d'étude, nous comparons deux types d 'adaptation : une adaptation basée sur des règles, et une adaptation basée sur notre approche dynamique. Nous montrons les différences entre ces deux approches, et illustrons les avantages de l'approche dynamique lorsque les règles sont imprécises. / Gesture learning is a complex and multi-step process where trainees are supposed to improve several psychomotor and cognitive skills. This process can be divided into phases depending on trainees’ ability to perform and perceive their gestures. As human tutors adapt their behavior according to their perception and understanding of trainees learning situations' a learning environment should select an appropriate behavior from a representation of trainees’ learning states and a prediction of the potential influence of every possible behavior. The work presented in this document describes an approach modeling the interactions between a trainee and a learning environment: it represents trainees’ consecutive performances and the influence of the environment on these performances. This approach aims at permitting an adaptive selection of the pedagogical actions (i.e. behaviors) implemented in the environment. Relying on related works in the domains of gesture recognition and intelligent tutoring systems, we propose to represent the gestural activity in a metric space. This representation interprets results from a probabilistic mode as fuzzy variables highlighting trainees' level on every aspect of the gesture. We represent the environment as the set of actions it can select, every action being represented by a feature vector describing the learning situation maximizing the action's influence. As a first step, these features are given by a set of experts, and we consider the rules provided as perfect. As a second step, we study an ubiquitous issue in the field of learning environments, which is the difference between the rules provided by experts and the reality of trainees’ needs toward feedback. Ill-defined domains are indeed more and more common, as collecting expert knowledge is difficult and costly, and as studied learning domains are becoming more and more complex and difficult to define. In this second step, the selection process does not rely on expert knowledge, and this process can be seen as a sequence of decisions. At each iteration, the goal is thus to select the action which would maximize the reward in terms of benefits for trainees' learning. The action selection process is represented as a multi-armed bandit problem, where the goal is to compromise between exploration of unknown actions and exploitation of known actions. We present an extension of SoftMax methods which handles multi-dimensional contextual rewards. Taking advantage of the collaboration with calligraphy experts, a calligraphy training platform was implemented as part of this work. Two studies, where participants train on this platform, show the benefits of the proposed approach on calligraphy learning. In a first experiment, action selection is based on expert rules, and we show that providing a diversity of feedback improves skill acquisition. In a second experiment, we compare two configurations of the environment: a selection of actions based on expert rules, and a selection of actions based on the SoftMax method. We describe the difference between the influence of these two approaches on trainees' learning, and we point out the benefits of using dynamic rules.

abstract: The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people's daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of "curse of choices", resulting in the difficulty for a user to make a satisfied decision on "where to go" in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing. Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects. Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014