With the rapid growth of biomedical literature in PubMed – about two articles every minute – finding and retrieving the most relevant papers for a given query is increasingly challenging. We demonstrate that Best Match, a recently introduced relevance search in PubMed, provides state-of-the-art retrieval performance in online experiments. Particularly, we find that this positive algorithmic change translates into increased click-through rate and improved user experience in real-world circumstances. Since the new algorithm was fully deployed in June 2017, we have also observed a steady increase (over 30%) in Best Match usage by PubMed users: assisting millions of searches in PubMed on a weekly basis.

Learning Objective 1: Learning the impact of relevance search for users on PubMed.

Learning Objective 2: Discovering online metrics used to evaluate systems and the performances of Best Match in PubMed.


Nicolas Fiorini (Presenter)

Zhiyong Lu, NIH

Presentation Materials: