On-the-fly Data Integration for Personalized Television Recommender Systems

In this paper we present our iFanzy demonstrator, which aims at offering users television content in a personalized and context-sensitive way. With iFanzy we integrate different volatile content sources like video-on-demand, broadcast and Web information on-the-fly to disperse it on different clients on different platforms like set-top boxes and Web interfaces. Via semantic Web techniques, TV content and background data from various heterogeneous sources is integrated into a transparent RDF/OWL knowledge graph, which allows the user to navigate and browse the vast content sets nowadays available. Via illustrative examples we explain how the main integrated data set is built and how we exploit it to provide iFanzypsilas main functionality, like semantic search of the available content and execution of context sensitive recommendations.