GUMCARS: General User Model for Context-Aware Recommender Systems

Context-Aware Recommender Systems (CARS) are extensions of traditional recommender systems that use information about the context of the user to improve the recommendation accuracy. Whatever the specific algorithm exploited by the CARS, it can provide high-quality recommendations only after having modeled the user and context aspects. Despite the importance of the data models in CARS, nowadays there is a lack of models and tools to support the modeling and management of the data when developing a new CARS, leaving designers, developers and researchers the work of creating their own models, which can be a hard and time-consuming labor, and often resulting in overspecialized or incomplete models. In this paper, we describe GUMCARS - a General User Model for Context-Aware Recommender Systems, where the main goal is to help designers and researchers when creating a CARS by providing an extensive set of User, Context and Item aspects that covers the information needed by different recommendation domains. To validate GUMCARS, two experiments are performed; first, the completeness and generality of the model are evaluated showing encouraging results as the proposal was able to support most of the information loaded from real-world datasets. Then the structural correctness of the model is assessed, the obtained results strongly suggest that the model is correctly constructed according to Object-Oriented design paradigm

A Framework for Exploiting Internet of Things for Context-Aware Trust-based Personalized Services

In the last years, we have witnessed the introduction of Internet of Things as an integral part of the Internet with billions of interconnected and addressable everyday objects. On the one hand, these objects generate massive volume of data that can be exploited to gain useful insights into our day-to-day needs. On the other hand, context-aware recommender systems (CARSs) are intelligent systems that assist users to make service consumption choices that satisfy their preferences based on their contextual situations. However, one of the major challenges in developing CARSs is the lack of functionality providing dynamic and reliable context information required by the recommendation decision process based on the objects that users interact with in their environments. Thus, contextual information obtained from IoT objects and other sources can be exploited to build CARSs that satisfy users’ preferences, improve quality of experience and recommendation accuracy. This article describes various components of a conceptual IoT based framework for context-aware personalized recommendations. The framework addresses the weakness whereby CARSs rely on static and limited contextual information from user’s mobile phone, by providing additional components for reliable and dynamic contextual information, using IoT context sources. The core of the framework consists of context recognition and reasoning management, dynamic user profile model incorporating trust to improve accuracy of context-aware personalized recommendations. Experimental evaluations show that incorporating context and trust in personalized recommendations can improve its accuracy

Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Model

Smart devices, such as smartphones, smartwatches, etc., are examples of promising platforms for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities on these platforms due to interclass pattern similarities, which occur when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these devices. Therefore, apart from using information from the traditional sensors, these systems lack the contextual information to support automatic activity recognition. In this article, we explore environmental contexts, such as illumination (light conditions) and noise level, to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) learning models. The models performed sensor fusion by augmenting low-level sensor signals with rich contextual data to improve the models’ recognition accuracy and generalization. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used triaxial inertial sensing signals to train baseline models, while the second set of experiments combined the inertial signals with contextual information from environmental sensors. The obtained results demonstrate that contextual information, such as environmental noise level and light conditions using hybrid deep learning models, achieved better recognition accuracy than the traditional baseline activity recognition models without contextual information

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC

Context-aware media recommendations for smart devices

© 2014, Springer-Verlag Berlin Heidelberg. The emergence of pervasive computing, the rapid advancements in broadband and mobile networks and the incredible appeals of smart devices are driving unprecedented universal access and delivery of online-based media resources. As more and more media services continue to flood the Web, mobile users will continue to waste invaluable time, seeking content of their interest. To deliver relevant media items offering richer experiences to mobile users, media services must be equipped with contextual knowledge of the consumption environment as well as contextual preferences of the users. This article investigates context-aware recommendation techniques for implicit delivery of contextually relevant online media items. The proposed recommendation services work with a contextual user profile and a context recognition framework, using case base reasoning as a methodology to determine user’s current contextual preferences, relying on a context recognition service, which identifies user’s dynamic contextual situation from device’s built-in sensors. To evaluate the proposed solution, we developed a case-study context-aware application that provides personalized recommendations adapted to user’s current context, namely the activity he/she performs and consumption environment constraints. Experimental evaluations, via the case study application, real-world user data, and online-based movie metadata, demonstrate that context-aware recommendation techniques can provide better efficacy than the traditional approaches. Additionally, evaluations of the underlying context recognition process show that its power consumption is within an acceptable range. The recommendations provided by the case study application were assessed as effective via a user study, which demonstrates that users are pleased with the contextual media recommendations