RECOMMENDATION SYSTEM IN AN AUDIOVISUAL DELIVERY PLATFORM

One of the main tasks of the information services is to help users to find information that satisfies their preferences reducing their search effort. Recommendation systems filter information and only show the most preferred items. Ontologies are fundamental elements of the Semantic Web and have been exploited to build more accurate and personalized recommendations by inferencing missing user preferences. With catalogs changing continuously ontologies must be built autonomously without expert intervention. In this paper we present an audiovisual recommendation engine which uses an enhanced ontology filtering technique to recommend audiovisual content. Experimental results show that the improvements of the ontology filtering technique generate accurate recommendations.