Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-07-12T13:47:04Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) risethesis.pdf: 1393384 bytes, checksum: f5f2fb9182ce60a9c5d2b0cd95f2893a (MD5) / Made available in DSpace on 2017-07-12T13:47:04Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) risethesis.pdf: 1393384 bytes, checksum: f5f2fb9182ce60a9c5d2b0cd95f2893a (MD5) Previous issue date: 2016-06-29 / Em meio a grande sobrecarga de dados disponíveis na internet, sistemas de recomendação tornam-se ferramentas indispensáveis para auxiliar usuários no encontro de itens ou conteúdos relevantes. Diversas técnicas de recomendação são aplicadas em diversos tipos de domínios diferentes. Seja na recomendação de filmes, música, amigos, lugares ou notícias, sistemas de recomendação exploram diversas informações disponíveis para aprender as preferências dos usuários e promover recomendações úteis. Uma das estratégias mais utilizadas é a de filtragem colaborativa. A qualidade dessa estratégia depende da quantidade de avaliações disponíveis e da qualidade do algoritmo utilizado para predição de avaliação. Estudos recentes demonstram que informações provenientes de redes sociais podem ser muito úteis para aumentar a precisão das recomendações. Assim como acontece no mundo real, no mundo virtual usuários buscam recomendações e conselhos de amigos antes de comprar um item ou consumir algum serviço, informações desse tipo podem ser úteis para definição do contexto social da recomendação. Além do social, informações físicas e temporais passaram a ser utilizadas para definição do contexto físico de cada recomendação. A companhia, a localização e as condições climáticas são bons exemplos de elementos físicos que levam um usuário a preferir certos itens. Um processo de recomendação que não leve em consideração elementos contextuais pode fazer com que o usuário tenha uma péssima experiência consumindo determina do item recomendado equivocadamente. Esta dissertação tem como objetivo investigar técnicas de filtragem colaborativa que utilizam contexto a fim de realizar recomendações que auxiliem usuários no encontro de itens relevantes. Nesse tipo de técnica, um sistema de recomendação base é utilizando para fornecer recomendações para o usuário alvo. Em seguida, são filtrados apenas os itens considerados relevantes para contextos previamente identificados nas preferências do usuário alvo. As técnicas implementadas foram aplicadas em dois experimentos com duas bases de dados de domínios diferentes: uma base composta por eventos e outra por filmes. Na recomendação de eventos, investigamos o uso de contextos físicos (i.e., tempo e local) e de contextos sociais (i.e., amigos na rede social) associados aos itens sugeridos aos usuários. Na recomendação de filmes, por sua vez, investigamos novamente o uso de contexto social. A partir da aplicação de pós-filtragem em três algoritmos de filtragem colaborativa usados como base, foi possível recomendar itens de forma mais precisa, como demonstrado nos experimentos realizados. / The overload of data available on the internet makes recommendation systems become indispensable tools to assist users in meeting items or relevant content. Several recommendation techniques were has been userd in many different types of domains. Those systems can recommend movies, music, friends, places or news; recommender systems can exploit different information available to learn preferences of users and promote more useful recommendations. The collaborative filtering strategy is one of the most used. The quality of this technique depends on the number of available ratings and the algorithm used to predict. Recent studies show that information from social networks can be very useful to increase the accuracy recommendations. Just as in the real world, the virtual world users ask recommendations and advice from friends before buying an item or consume a service. Furthermore, the context of each rating may be crucial for the definition of new ratings. Location, date time and weather conditions are good examples of useful elements to define what should be the best items to recommend for some user. A recommendation process that does not respect those elements can provide a user a bad experience. This dissertation investigates collaborative filtering techniques based on context, and more specifically techniques based on post-filtering. First, a recommendation system was used to provide recommendations for a specific user. Then, only relevant items according to context preferences for the target user will be recommended. The techniques implemented was applied in two case studies with two different domains databases: one base composed of events and another of movies. In the event of recommendation, we investigated the use of physical contexts (i.e., time and place) and social contexts (i.e., friends in the social network) associated with items suggested to users. On the recommendation of movies, in turn, again we investigated the use of social context. From the application of post-filtering in three collaborative filtering algorithms used as a baseline, it was possible to recommend items more accurately, as demonstrated in the experiments.

The present study examined whether cardiovascular habituation to stress is affected by a change in the physical context in which a stressor is encountered. Twenty-five undergraduate students at the University of Central Florida, Palm Bay Campus, were exposed to 4 trials of a stressor consisting of mental arithmetic while under evaluative observation. It was hypothesized that if participants experienced a change in the physical context in which stress was experienced on the final trial, they would demonstrate impaired habituation to stress as indicated by measures of heart rate and blood pressure. Physical context was manipulated by either asking participants to move to another room upon the final exposure to the stressor or to remain in the same room in which they were initially exposed to the stressor for the final exposure. Participants were randomly assigned to one of 2 conditions, the Stable Room Condition (N = 10) or Novel Room condition (N = 15 ). Participants in the Stable Room Condition remained in the same physical context, or same room, throughout all trials and displayed habituation of systolic .blood pressure, diastolic blood pressure, and heart rate. Participants in the Novel Room condition were exposed to the same stressors, but were moved to a different physical context, or new room upon the final trial. The results demonstrated that participants in the novel room condition displayed significantly impaired habituation on measures on systolic blood pressure (p < .001) and diastolic blood pressure (p < .001). However, no significant difference in heart rate was observed between groups. These results indicate that a simple change in the physical context in which stress exposure occurs impairs cardiovascular habituation to stress. Implications and directions for future research are discussed.