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In this paper a recommender system of personalized TV contents, named AVATAR 1 , is presented. We propose a modular multi-agent architecture for the system, whose main novelty is the semantic reasoning about user preferences and historical logs, to improve the traditional syntactic content search. Our approach uses Semantic Web technologies – more(More)
In this paper we introduce a cooperative environment between the Interactive Digital TV (IDTV) and home networking with the aim of allowing the interaction between interactive TV applications and the controllers of the in-home appliances in a natural way. More specifically, our proposal consists of merging MHP (Multimedia Home Platform), one of the main(More)
— The generalized arrival of the Digital TV will bring a significant increase in the amount of channels and programs available to end users, with many more difficulties for them to find interesting programs among a myriad of irrelevant contents. Thus, automatic content recommenders should receive special attention in the following years to improve the(More)
In last decade, we have seen the first steps to the end of passive television. Thanks to continuous advances in hardware and software, now, Digital TV technology is mature enough to enhance traditional TV sets (limited to content reproduction) with computing capability to run multimedia software integrating richer formats. And a significant example of this(More)
Tourism recommender systems match the user preferences against the huge diversity of tourist resources, helping to decide where to go and what to do. Current approaches require the users to initialize manually their profiles by expressing their interests accurately, which is a very tedious process. We propose a system that automatically infers the users’(More)