Enhanced re-ranking of our recommendations based on Mendeley’s readership statistics

Content-based filtering suffers from the problem that no human quality assessments are taken into account. This means, a poorly written paper ppoor would be considered equally relevant for a given input paper pinput as high-quality paper pquality if pquality and ppoor contain the same words. We elevate for this problem by using Mendeley’s readership data for re-ranking Mr. DLib’s […]

New recommendation algorithms integrated

We have integrated several new recommendation algorithms into Mr. DLib. Some algorithms are only ought as baselines for our researchers, others hopefully will further increase the effectiveness of Mr. DLib. Overall, Mr. DLib now uses the following algorithms: Random The approach randomly picks the set of documents to recommend to the user. We experiment with this […]

Two new servers are online (dev and beta system)

So far, Mr. DLib’s recommender system was running only on a single server. Consequently, when me messed up something in the development environment, sometimes the production system was affected, i.e. down. From today on, we have two additional dedicated servers running, meaning we have a total of three servers, one for the development, one for beta, and […]

First Pilot Partner (GESIS’ Sowiport) Integrates Mr. DLib’s Recommendations as-a-Service

We are proud to announce that the social science portal Sowiport is using Mr. DLib as first pilot partner. Sowiport pools and links quality information from domestic and international providers, making it available in one place. Sowiport currently contains 9.5 million references on publications and research projects. The documents in Sowiport comprise bibliographic metadata (such […]