Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia
This addresses information overload for academic users by enabling easy integration of recommender systems, though it is incremental as it builds on existing methods.
The paper tackled the lack of recommender systems in academic tools by introducing Mr. DLib's recommendations-as-a-service, which delivered 57 million recommendations to a partner platform.
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.