  Only few digital libraries and reference managers offer recommender systems,
although such systems could assist users facing information overload. In this
paper, we introduce Mr. DLib's recommendations-as-a-service, which allows third
parties to easily integrate a recommender system into their products. We
explain the recommender approaches implemented in Mr. DLib (content-based
filtering among others), and present details on 57 million recommendations,
which Mr. DLib delivered to its partner GESIS Sowiport. Finally, we outline our
plans for future development, including integration into JabRef, establishing a
living lab, and providing personalized recommendations.
