Supervision : Jean-Gabriel GANASCIA

Multidimensional television programs Recommendation by Machine Learning, an EPG Intelligent visualization interface for digital television

Due to the wealth of entertainment contents provided by Digital Mass Media and in particular by Digital Television (satellite, cable, terrestrial or IP), choosing a program has become more and more difficult. Far from having a user-friendly environment, Digital Television (DTV) users face a huge choice of content, assisted only by off-putting interfaces named classical "Electronic Program Guide" EPG. That makes users' attention blurry and decreases their active program searching and choice. The central topic of this thesis is the development of a Recommendation System interfaced mapping interactive TV content.
To do this, we chose to use a Recommendation System based on the content and have adapted to the field of television. This adaptation is carried out at several specific steps. We especially worked processing metadata associated with television content and developing an expert system can provide us with a unique categorization of television. We also took the initiative to model and integrate the context of use in our television viewing environment modeling. The integration of context allowed us to obtain a sufficiently fine and stable in this environment, allowing us to implementing our recommendation system.
Detailed categorization of metadata associated with television content and modeling & integration of context of use television is the main contribution of this thesis. To assess / improve our developments, we installed a fleet of nine homes left in three specific types of families. This has given us the means to assess the contribution of our work in ease of use television in real conditions of use. By an implicit approach, we apprehended the behavior of television families (involved in our project) vis-à-vis television content. A syntactic-semantic analyzer has provided a measure of gradual interest thereon to the content, for each family. We have also developed an interactive mapping interface based on the idea of "Island of memory" for the interactive interface is in line with Recommendation System in place.
Our recommendation system based on content and assisted learning (reinforcement learning), has provided us with the most optimal results to the scientific community in the field.

2008-2013 Publications

• 2013
  • K. Fouladi : “Recommandation multidimensionnelle d’émissions télévisées par apprentissage, une interface de visualisation intelligente pour la télévision numérique”, thesis, phd defence 01/24/2013, supervision Ganascia, Jean-Gabriel (2013)
• 2010
  • W. Turner, S. Buisine, J.‑G. Ganascia, L. Eveque, K. Fouladi, J. Marlier, M. Retamales : “La conception innovante des objets à intelligence incorporée”, chapter in Technologies de l'information et intelligences collectives, Systèmes d'information et organisations documentaires, pp. 37-62, (Hermès & Lavoisier) (2010)
  • S. Buisine, K. Fouladi, J. Nelson, William A. Turner : “Optimiser le processus d’innovation grâce aux traces informatiques d’usages”, IC 2010 - 21^es Journées francophones d'Ingénierie des Connaissances, Nîmes, France, pp. 145-156, (Ecole des Mines d'Alès) (2010)
• 2009
  • S. Buisine, L. Eveque, K. Fouladi, J. Marlier, W. Turner : “Représentation d’un espace de valeurs d’usage pour guider la conception”, 20^es Journées Francophones d'Ingénierie des Connaissance, Hammamet, Tunisia (2009)
• 2008
  • J.‑G. Ganascia, Ch. Madeira, K. Fouladi : “An Adaptive Cartography of DTV Programs”, EuroITV 2008 - 6^th European Conference on Changing Television Environments, vol. 5066, Lecture Notes in Computer Science, Salzburg, Austria, pp. 253-262, (Springer-Verlag) (2008)