Content-based filtering recommendations suffer 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 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.