This paper details and discusses experimental results obtained towards personalizing a museum visit through a personal mobile guide, using an approach relying on users' cognitive style, gaming... Show More

This paper details and discusses experimental results obtained towards personalizing a museum visit through a personal mobile guide, using an approach relying on users' cognitive style, gaming, social networks, and recommendations. It describes the personalization system, which relies on a Facebook game to infer users' cognitive style, visiting style and interests, and a recommendation algorithm offering sequences of points of interests to visit. A qualitative and quantitative analysis of an experiment conducted in a museum is given, offering first conclusions and perspectives on the approach.

This paper presents an approach to enhance users experience through the use of recommendations and social networks for on-the-fly (at runtime) adaptation of multimedia documents. This paper presents... Show More

This paper presents an approach to enhance users experience through the use of recommendations and social networks for on-the-fly (at runtime) adaptation of multimedia documents. This paper presents also CSSAP, a dynamic service selection and assembly tool based on new user profiles and community profiles defined as set of semantic metadata, which context, quality of service and quality of experience parameters. The tool is based on community-aware semantic services and offer architecture, with three layers (semantic query, community management and semantic services). The most innovative characteristic of the tool is that it profits from the potential of semantic representation techniques to express context constraints and community's interests, while they may be useful to generate and manage of complex dynamic adaptation process. This tool improves assembly of relevant adaptation services for communities inferred social influence from a Facebook as virtual P2P environment. The proposed approach has been validated through a prototype for mobiles user of multimedia contents exchanges. The goal is to improve assembly of potential adaptation services and the efficiency and effectiveness of the authors' approach.

Social networking apps, sites and technologies offer a wide range of opportunities for businesses and developers to exploit the vast amount of information and user-generated content produced through... Show More

Social networking apps, sites and technologies offer a wide range of opportunities for businesses and developers to exploit the vast amount of information and user-generated content produced through social networking. In addition, the notion of second screen TV usage appears more influential than ever, with viewers continuously seeking further information and deeper engagement while watching their favourite movies or TV shows. In this work, the authors present SAM, an innovative platform that combines social media, content syndication and targets second screen usage to enhance media content provisioning, renovate the interaction with end-users and enrich their experience. SAM incorporates modern technologies and novel features in the areas of content management, dynamic social media, social mining, semantic annotation and multi-device representation to facilitate an advanced business environment for broadcasters, content and metadata providers, and editors to better exploit their assets and increase their revenues.

In this article, the authors try to predict the next-day CAC40 index. They apply the idea of Johan Bollen et al. from (Bollen, Mao, & Zeng, 2011) on the French stock market and they conduct their... Show More

In this article, the authors try to predict the next-day CAC40 index. They apply the idea of Johan Bollen et al. from (Bollen, Mao, & Zeng, 2011) on the French stock market and they conduct their experiment using French tweets. Two analyses are applied on tweets: sentiment analysis and subjectivity analysis. Results of these analyses are then used to train a simple neural network. The input features are the sentiment, the subjectivity and the CAC40 closing value at day-1 and day-0. The single output value is the predicted CAC40 closing value at day+1. The authors propose an architecture using the JEE framework resulting in a better scalability and an easier industrialization. The main experiments are conducted over 5 months of data. The authors train their neural network on the first of the data and they test predictions on the remaining quarter. Their best run gives a direction accuracy of 80% and a mean absolute percentage error (MAPE) of 2.97%. In another experiment, the authors retrain the neural network each day which decreases the MAPE to 1.14%.