Alberto Gil-Solla

TL;DR: A new approach for automatic content recommendation is presented, based on the so-called semantic Web technologies, that significantly reduces deficiencies of current approaches of content recommenders.

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: This paper presents a strategy that overcomes overspecialization by applying reasoning techniques borrowed from the semantic Web, and discovers a huge amount of knowledge about the user's preferences, and compares them with available products in a more flexible way, beyond the conventional syntactic metrics.

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: This paper presents a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations to fulfill the personalization requirements of recommender systems.