A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring

[EN]Real-time Localization Systems have been postulated as one of the most appropriated technologies for the development of applications that provide customized services. These systems provide us with the ability to locate and trace users and, among other features, they help identify behavioural patterns and habits. Moreover, the implementation of policies that will foster energy saving in homes is a complex task that involves the use of this type of systems. Although there are multiple proposals in this area, the implementation of frameworks that combine technologies and use Social Computing to influence user behaviour have not yet reached any significant savings in terms of energy. In this work, the CAFCLA framework (Context-Aware Framework for Collaborative Learning Applications) is used to develop a recommendation system for home users. The proposed system integrates a Real-Time Localization System and Wireless Sensor Networks, making it possible to develop applications that work under the umbrella of Social Computing. The implementation of an experimental use case aided efficient energy use, achieving savings of 17%. Moreover, the conducted case study pointed to the possibility of attaining good energy consumption habits in the long term. This can be done thanks to the system’s real time and historical localization, tracking and contextual data, based on which customized recommendations are generated.European Commision (EC). Funding H2020/MSCARISE. Project Code: 64179

Recommender systems and their ethical challenges

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system

Modeling social information skills

In a modern economy, the most important resource consists in\ud human talent: competent, knowledgeable people. Locating the right person for\ud the task is often a prerequisite to complex problem-solving, and experienced\ud professionals possess the social skills required to find appropriate human\ud expertise. These skills can be reproduced more and more with specific\ud computer software, an approach defining the new field of social information\ud retrieval. We will analyze the social skills involved and show how to model\ud them on computer. Current methods will be described, notably information\ud retrieval techniques and social network theory. A generic architecture and its\ud functions will be outlined and compared with recent work. We will try in this\ud way to estimate the perspectives of this recent domain

IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Analysis and control of complex collaborative design systems

This paper presents a novel method for modelling the complexity of collaborative design systems based on its analysis and proposes a solution to reducing complexity and improving performance of such systems. The interaction and interfacing properties among many components of a complex design system are analysed from different viewpoints and then a complexity model for collaborative design is established accordingly. In order to simplify complexity and improve performance of collaborative design, a general solution of decomposing a whole system into sub-systems and using unified interface mechanism between them has been proposed. This proposed solution has been tested with a case study. It has been shown that the proposed solution is meaningful and practical