Biblio

The representation of structural data is important to capture the pattern between features. Interrelations between variables provide information beyond the standard variables. In this study, we show how ontology information may be used in a recommender systems to increase the efficiency of predictions. We propose two alternative similarity measures that incorporates the structural data representation. Experiments show that our ontology-based approach delivers improved classification accuracy when the dimension increases.