Extending SPARQL for Recommendations

Abstract

For processing data on the Web, recommender systems and SPARQL are two popular paradigms, which however have rather different characteristics. SPARQL is a declarative language on RDF graphs which allows a user to precisely specify the desired information. In contrast, a recommender system suggests certain items to a user, based on similarity to other users or items. As the data to be processed by a recommender may be an RDF graph as well, the question arises whether both processing paradigms can benefit from each other. RecSPARQL fills this gap by extending the syntax and semantics of SPARQL to enable a generic and flexible way for collaborative filtering and content-based recommendations over arbitrary RDF graphs. Our experiments on the MovieLens data set demonstrate the applicability of our approach.