View article

Recommender Systems (RS) benefit from the richly-structured information contained in publicly available RDF-graphs. Not only is the burden of extracting features from text thereby alleviated, but also the interconnections in the graph have been proven to be useful. These allow one to find related items in the graph which do not necessarily share a large number of features, eg items from different domains. Thus, these graphs can be exploited to generate cross-domain recommendations, ie to recommend items using feedback provided in a different domain. To benefit from RDF’s data model, RecSPARQL has been proposed as an extension of SPARQL together with a system which evaluates such queries. Although this solution makes it possible to generate recommendations on top of arbitrary RDF-graphs, it is limited to single-domain recommendations. In this paper we present an extension of RecSPARQL’s syntax and semantics for cross-domain recommendations. Our experiments on a very sparse dataset show that the added components can help to improve the quality of recommendations.