AVATAR: an improved solution for personalized TV based on semantic inference

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 assistance to users. Current approaches of content recommenders have important well-known deficiencies, which difficult their wide acceptance. In this paper, a new approach for automatic content recommendation is presented, based on the so-called semantic Web technologies, that significantly reduces those deficiencies. The approach has been implemented in the AVATAR tool, a hybrid content recommender that makes extensive use of well-known standards, such as MHP, TV-anytime, or OWL.

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