Recomendação personalizada dinâmica de informação sobre serviços públicos e sociais na iTV para seniores : um estudo de caso

A difusão e o acesso adequado à informação sobre Serviços de Interesse Geral são direitos constitucionais dos cidadãos e integram fatores determinantes na estruturação de uma sociedade mais igualitária e baseada na democratização do conhecimento. No entanto, não obstante a crescente quantidade de informação disponível e a evolução das TIC, verifica-se que o cidadão sénior, muitas vezes caraterizado pelos seus baixos níveis de literacia digital e infoinclusão, tem frequentemente dificuldade em aceder a informações sobre políticas e serviços públicos e sociais dos quais pode beneficiar. Com necessidades informacionais específicas e cada vez mais tempo livre decorrente da reforma, os seniores tendem a utilizar a TV como meio primordial de informação e entretenimento. Deste modo, beneficiando da familiaridade deste público com a TV, muitas soluções tecnológicas inovadoras têm-se baseado neste dispositivo. No entanto, apenas conceber e empregar recursos tecnologicamente avançados não é suficiente. É, sim, preciso elaborar soluções personalizadas, que possam melhor adaptar-se às preferências e limitações deste segmento populacional. Neste caso concreto, tal trata-se de identificar qual a informação mais adequada a ser enviada a cada sénior. Por exemplo, informações sobre campanhas de saúde e descontos em taxas moderadoras devem ser enviadas conforme as preferências e o contexto (e.g. localização) do utilizador. Este trabalho propõe uma estratégia de personalização para a entrega de conteúdos informativos sobre Serviços de Interesse Geral, em um ambiente televisivo, para a população sénior. Para tal, este trabalho tem por objetivo alavancar a exibição de vídeos informativos através da integração de um Sistema de Recomendação Sensível ao Contexto (CARS). A investigação dividiu-se em três etapas distintas, numa abordagem de design participativo, de modo que o CARS seja adequado às especificidades deste segmento populacional, considerando as opiniões e indicações de vários seniores em todas as fases do estudo. Na primeira etapa, são caracterizados os dados do trinómio [Item x Utilizador x Contexto]. Esta etapa decorreu com colaboração de especialistas nas áreas de gerontologia, serviços públicos e TV Interativa, bem como com a colaboração de seniores recrutados no âmbito do projeto +TV4E, a partir da aplicação de entrevistas, focus groups e testes guiados. Na segunda etapa, é proposto o CARS de acordo com o Modelo de dados e o esquema de interação obtidos a partir dos resultados provenientes da etapa anterior. Um algoritmo de recomendação híbrido é proposto para gerar as recomendações. Por fim, na terceira e última etapa, foi desenvolvido um protótipo, integrado no projeto +TV4E, de modo a validar o CARS, em ambiente doméstico, por um período de duas semanas e com o apoio de 21 seniores residentes no distrito de Aveiro. A análise dos resultados, a partir dos registos de utilização do protótipo e de entrevistas, corroboram a utilidade e adequabilidade da estratégia de personalização proposta.The dissemination and adequate access to information about Services of General Interest are constitutional rights of the citizens and play a major role in structuring a more egalitarian society based on the democratization of knowledge. However, despite the increasing amount of information available and the evolution of information and communication technologies (ICT), senior citizens, often characterized by lower levels of digital literacy and info-inclusion, often struggle to access information about policies and services that they can benefit from. With specific informational needs and free time due to retirement, seniors tend to use TV as a primary mean of information and entertainment. In this way, benefiting from the familiarity of these citizens with the TV, many innovative technological solutions have been leveraged this device. However, solely designing and employing technologically advanced features is not enough. It is necessary to develop personalized solutions to better adapt to seniors’ preferences and limitations. In this case, this concerns identifying which information is more appropriate to be provided for each senior. For example, information on health campaigns and social tariffs discounts should be tailored according to the user’s specific preferences and contextual factors (e.g. location and dates). That said, this research proposes a personalization strategy for the delivery of highvalued informative contents about Services of General Interest for the senior population. To this end, this work aims to leverage the informative videos exhibition through the integration of a Context-Aware Recommender System (CARS). The investigation was divided into three distinct phases, in a participatory design approach, so that the CARS is adequate to the specifics of this population segment, considering seniors’ opinions and indications in all phases of the study. In the first phase, data of the trinomial [Item x User x Context] is characterized. In addition, this phase was carried out with the collaboration of specialists in the areas of gerontology, public services, interactive TV and software engineering, as well as the collaboration of seniors recruited under the + TV4E project, through the application of interviews, focus groups and guided tests. In the second phase, the CARS is proposed according to the Data Model and the interaction scheme obtained from the results of the previous phase. A hybrid filtering algorithm is proposed to generate the recommendations. Finally, in the third and last phase, a prototype was developed and integrated in the scope of + TV4E project, in order to validate the CARS, in a domestic environment, for a period of two weeks, and with the support of 21 senior residents in the district of Aveiro. The analysis of the results, based on user interactions and interviews, corroborate the usefulness and appropriateness of the personalization strategy proposed by CARS.Programa Doutoral em Informação e Comunicação em Plataformas Digitai

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost