Content-based filtering suffers from the problem that no human quality assessments are taken into account. This means, a poorly written paper p^poor would be considered equally relevant for a given input paper p^input as high-quality paper p^quality if p^quality and p^poor contain the same words.

We elevate for this problem by using Mendeley’s readership data for re-ranking Mr. DLib’s recommendations. This means, once we have a number of e.g. 20 documents that are related for a requested input paper, we re-rank the 20 documents based on the number of readers they have on Mendeley. The most read papers are then recommended. More details will follow.