Schema-Aware feature selection in Linked Data-based recommender systems

Semantics-Aware recommendation engines have emerged as a new family of systems able to exploit the semantics encoded in un- structured and structured information sources to provide better results in terms of accuracy, diversity and novelty as well as to foster the pro- visioning of new services such as explanation. In the rising of these new recommender systems, an important role has been played by Linked Data (LD). However, as Linked Data is often very rich and contains many in- formation that may result irrelevant and noisy, an initial step of feature selection may be required in order to select the most meaningful portion of the original dataset. Many approaches have been proposed in the literature for feature selection that exploit different statistical dimensions of the original data. In this paper we investigate the role of the semantics encoded in an ontological hierarchy via schema-summarization when exploited to select the most relevant properties for a recommendation task.