Using Ontology-based Data Summarization to Develop Semantics-aware Recommender Systems?

In the current information-centric era, recommender systems are gaining momentum as tools able to assist users in daily decision-making tasks. Within the recommendation process, Linked Data have been already proposed as a valuable source of information to enhance the predictive power of recommender systems but an open issues is still related to feature selection of the most relevant subset of data in the whole semantic web. In this paper, we show how ontology-based (linked) data summarization can drive the selection of properties/features useful to a recommender system. In particular, we compare a fully automated feature selection method based on ontology-based data summaries with more classical ones, and we evaluate the performance of these methods in terms of accuracy and aggregate diversity of a recommender system exploiting the top-k selected features.