A Recommender System Infrastructure to Allow Educational Metadata Reasoning

This work presents a recommender infrastructure for educational material that is described with metadata. For its operation we propose the use of the OBAA standard, an extension to IEEE LOM that provides interoperability among hardware platforms. We also suggest the need in reusing the user profiles information that are available through FOAF metadata and extend them with personalized educational information. In order to test this infrastructure, we proposed Lassique, an application that makes reasoning over a metadata ontology to filter educational material suggested by a Collaborative Filtering Algorithm.