Flexible on-the-fly recommendations from Linked Open Data repositories

Abstract

Recommender systems help consumers to find products online. But because many content-based systems work with insufficient data, recent research has focused on enhancing item feature information with data from the Linked Open Data cloud. Linked Data recommender systems are usually bound to a predefined set of item features and offer limited opportunities to tune the recommendation model to individual needs. The paper addresses this research gap by introducing the prototype SKOS Recommender (SKOSRec), which produces scalable on-the-fly recommendations through SPARQL-like queries from Linked Data repositories. The SKOSRec query language enables users to obtain constraint-based, aggregation-based and cross-domain recommendations, such that results can be adapted to specific business or customer requirements.