Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work

Auto-regulação na gestão de conflitos em retalho

Mestrado em Engenharia InformáticaO sector do retalho ocupa actualmente um espaço significativo na economia dos países desenvolvidos. Ao longo das últimas décadas, este sector tem verificado uma grande transformação que consistiu no contínuo crescimento dos supermercados, hipermercados e grandes superfícies e na queda abrupta do pequeno retalho tradicional. Torna-se premente superar a concorrência e ultrapassar as expectativas dos clientes cada vez mais informados e exigentes. As tendências, os gostos e as preferências dos consumidores, a evolução das tecnologias combinam-se para gerar uma envolvente dinâmica traduzida numa mudança contínua. Com o desenvolvimento do comércio electrónico, a noção de concorrente também se alterou dado que as limitações de espaço tradicionais que serviam para definir a área de influências das lojas deixaram de existir. Com a constante evolução de negócio, optimização de estratégias e processos na área do retalho, surgiu um aumento significativo na complexidade inerente aos sistemas de informação que servem de suporte às infra-estruturas tecnológicas do retalhista. Nesta dissertação, dá-se uma contribuição para incrementar a capacidade e qualidade da resposta dos Sistemas de Retalho numa perspectiva de suporte à decisão. A contribuição proposta consiste na análise e adequação da capacidade de Auto-Regulação na Gestão de Conflitos em Retalho e na definição e desenvolvimento de mecanismos que proporcionem ao retalhista fiabilidade em termos de dados para as suas decisões e construção de estratégias de negócio. A Auto-Regulação e a Gestão de Conflitos em Retalho são consideradas duas áreas promissoras mas relativamente pouco exploradas. Neste sentido, é proposto o sistema Self-Regulation Retail (SelfRetail) capaz de lidar com o dinamismo e variação implícitos numa área complexa como o retalho. O módulo de Auto-Regulação comporta-se como um SAD resultado da inter-relação dos diversos componentes e agentes computacionais que funcionam de forma cooperante, com o qual se pretende permitir ao retalhista a eficiente adaptação à mudança, com a obrigatoriedade no cumprimento de um conjunto de restrições. As estratégias de representação do conhecimento podem evoluir com o desempenho do sistema, permitindo identificar com base no estudo dos seus parâmetros e do seu efeito em termos de negócio, as melhores regras ou restrições a aplicar nos diferentes cenários. Esta capacidade de ajuste/adaptação do conhecimento é realizado continuamente, dotando os agentes da capacidade de mudança comportamental sobre uma série de processos que no seu conjunto permitem delinear uma estratégia de negócio que evolui ao longo do tempo.The retail sector currently holds a significant place in the economies of developed countries. Over the past decades, this sector has verified a great transformation that consisted of the continuous growth of supermarkets, hypermarkets and the sharp decline of the small traditional retail. It is urgent to overcome the competition and exceed the customer expectations continuously increasing in terms of information and demand. Trends, tastes, consumer preferences and the evolving technologies are joint to generate a dynamic environment translated into a continuous change. With the development of electronic commerce, the idea of competition has also changed, since the traditional limitations of space that served to define the area of stores influence no longer exist. With the constant evolution of business optimization strategies and processes in the retail area, there was a significant increase in the complexity inherent in information systems which support the technological infrastructure of the retailer. In this thesis, it is provided a contribution to increase the capacity and quality response of Retail Systems from decision support perspective. The proposed contribution consists in the analysis and adequacy of the capacity of Self-Regulation in Conflict Management in Retail and in defining and developing mechanisms that provide the retailer with capacity in terms of consistency data for their decisions and to build business strategies. Self-Regulation and Conflict Management in Retail are considered two promising but relatively unexplored areas. In this sense, the proposed Self-Regulation Retail system (SelfRetail) is able of dealing with the dynamism and variation implicit in a complex area such as retail. The Self-Regulation module behaves like a DSS result of the interrelationship of the different components and computational agents that work in a cooperative way with which is intended to enable the retailer to effectively adapt to change, under an set of constraints. The strategies of knowledge representation can evolve with the system performance, allowing the identification based on a study of its parameters and its effect in terms of business, the best rules or restrictions to be applied in different scenarios. This adjustment capability / knowledge adaptation is performed continuously, providing agents with the ability to change behavior on a series of processes which together allow sketching a business strategy that evolves over time