Rebeca P. Díaz-Redondo

TL;DR: This paper proposes a personalization strategy that overcomes drawbacks in recommender systems by applying inference techniques borrowed from the Semantic Web, and illustrates its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.

TL;DR: A reasoning-based approach that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user's preferences and the available items improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations.

TL;DR: A framework for the development and deployment of t-learning services that promotes interoperability and reuse while taking into account the characteristic features of the IDTV medium is introduced.

TL;DR: This article compares several standardized run-time environments with nonstandardized solutions that aim to overcome constraints on the reusability of contents when dealing with intelligent content.

TL;DR: The adaptation possibilities of the SCORM standard are studied and an extension to permit adaptivity according to user's characteristics is presented, which comprises a syntax for adaptivity rules based on a set of adaptation parameters.