429 research outputs found

    Principles to Design Smart Physical Objects as Adaptive Recommenders

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    Recommenders have proven to be useful means to support people in their activities and in making decisions. They evolved from online recommenders to context-aware and ubiquitous recommenders. Moving forward along this line, this paper introduces the new emerging class of smart physical recommenders: context-aware recommender systems that are embedded into physical everyday objects. This paper describes the features of these systems and presents a conceptual model to design them, by analyzing a number of issues that have to be addressed by a designer and discussing the consequences of different design choices with their impact on the smartness of the designed object. The model is structured in a number of layers corresponding to different conceptual design phases in which different requirements are analyzed. The contribution of this paper is to discuss and provide design guidelines for a new rising class of recommenders that combine the features of intelligent agents, cyber-physical objects, and recommender-support systems. The description of the model is complemented by an exemplary analysis of its application

    Panorama of Recommender Systems to Support Learning

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    This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424). Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders (FWO). Olga C. Santos would like to acknowledge that her contributions to this work have been carried out within the project Multimodal approaches for Affective Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts (MAMIPEC -TIN2011-29221-C03-01). Nikos Manouselis has been partially supported with funding CIP-PSP Open Discovery Space (297229

    Design of an E-learning system using semantic information and cloud computing technologies

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    Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

    Recommender Systems for Healthy Behavior Change

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    Sedentary lifestyles and bad eating habits influence the onset of many serious health problems. Healthy behavior change is an arduous task, and requires a careful planning. In this thesis, we propose that behavior recommenders can help their users achieve healthy behavior change. Such a system should inspire its users with small, incremental and achievable goals. For this, it must resolve a trade-off between two opposing objectives: help the user achieve a steady improvement in target behavior, and avoid extreme goals that may injure or discourage the user. This is an unprecedented challenge in the recommender systems research. If the system understands the impacts of past interventions for behavior change, it can determine its usersâ behavioral responses to its own recommendations. This implies a specific data curation, in which we not only measure people's behavior but also deliberately introduce an intervention to monitor its effect on people's patterns. In turn, the system can use these existing users' information to derive the right procedure for effective recommendations. In this study we capitalize on this insight and develop InspiRE - our behavior recommender framework. Through InspiRE we propose the following contributions: 1) We design the data curation. 2) We develop the novel approaches for behavior profiling 3) We develop an evaluation process for this novel type of recommender system, and also compare it with traditional, similarity-based recommendation approach. We curate a dataset that contains information of daily step counts and social intervention for 83 people. InspiRE successfully uses the observations from this dataset, and proposes recommendations that are both effective and feasible. We also show that InspiRE can generalize to other dimensions of well being: we demonstrate this through a dataset that contains the snacking patterns of 73 people, who receive message-based interventions. We observe that InspiRE's recommendation strategy is in line with theories of behavior change

    Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

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    Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be ``holistic'', i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: rst, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best t the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of ``holistic'' RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles

    Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

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    Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching

    Media Literacy Education in the Age of Machine Learning

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    The media environment has radically changed over the past few decades. Transition and transformation of media platforms has enabled algorithms and automation to take over media processes such as production, content generation, curation, delivery, recommendation, and filtering of information. It has also enabled tracking of users’ actions, data mining, profiling, and the use of computational and machine learning techniques for purposes like behavior engineering, targeted advertisement, spread of mis- and disinformation, swaying political moods, and many others. In the field of media literacy education, the need to understand algorithm-driven media requires educators to re-think the connections between media literacy education and computing education. This article provides an overview of some computational mechanisms of new media, and it provides new perspectives for media literacy education. The article suggests ways of intertwining media literacy education with computing education in order to improve students’ readiness to cope with modern media and to become critical and skilled actors to navigate in the new media landscape

    User-centric IoT: challenges and perspectives

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    International audienceThe Internet of Things (IoT), this emerging technology connecting everyone, and everyone’s things’, is not about objects, gadgets, databases, applications and profits to be made from it, but about people, it enriches. Researchers, developers, industries, telecommunication companies, and scientific communities have been interested in this paradigm and have proposed different solutions from different perspectives. They are mainly focused on the technical level, like performance, interoperability, integration, etc. However, whenever use cases are targeting human users, the focus must not be merely on these sides, but on human factors as well. Thus, it is essential to apply a user-centric approach allowing identification of application-specific features and understanding users needs, motivations and beliefs. This survey aims at encouraging other IoT system developers and researchers to pay attention to the relationship between people and IoT systems. We emphasize the value of adopting a user-centric vision. The goal is not to provide solutions, but rather to raise the right issues

    Personalised service discovery in mobile environments

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    In recent years, some trends have emerged that pertain both to mobile devices and the Web. On one side, mobile devices have transitioned from being simple wireless phones to become ubiquitous Web-enabled users' companions. On the other side, the Web has evolved from an online one-size-fits-all collection of interlinked documents to become an open platform of personalised services and content. It will not be long before these trends will converge and create a Seamless Web: an integrated environment where, besides traditional services delivered by powerful server machines accessible via wide area networks, new services and content will be offered by users to users via their portable devices. As a result, mobile users will soon be exposed - in addition to traditional "on-line" Web services/content - to a parallel universe of pervasive "off-line" services provided by devices in their surroundings. Such circumstances will raise new challenges when it comes to selecting the services to rely on, that will require solutions grounded on the characteristics of mobile environments. Two aspects will require particular attention: first, users will have access to a countless multitude of services impossible to explore; they will need assistance to identify, among this multitude, those services they are most likely to enjoy. Secondly, if today's services (and their providers) are always-on, `static' and aiming at Five 9s availability, tomorrow's pervasive services will be mobile (as devices move), fine-grained, increasingly composite (to provide richer functionalities) and so more unreliable by nature. Our research tackles the problem of service discovery in pervasive environments in two ways: on one hand, we support personalised discovery by means of a mobile recommender system, easing the discovery of pervasive services appealing to end-users. On the other hand, we enable reliable discovery, by reasoning on the composite nature of pervasive services and the physical availability of their component providers. Overall, we provide a discovery method that enables 'better' pervasive services, where by 'better' we mean both `more interesting' to the user and 'more reliable'
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