  Obtaining funding is an important part of becoming a successful scientist.
Junior faculty spend a great deal of time finding the right agencies and
programs that best match their research profile. But what are the factors that
influence the best publication--grant matching? Some universities might employ
pre-award personnel to understand these factors, but not all institutions can
afford to hire them. Historical records of publications funded by grants can
help us understand the matching process and also help us develop recommendation
systems to automate it. In this work, we present \textsc{GotFunding} (Grant
recOmmendaTion based on past FUNDING), a recommendation system trained on
National Institutes of Health's (NIH) grant--publication records. Our system
achieves a high performance (NDCG@1 = 0.945) by casting the problem as learning
to rank. By analyzing the features that make predictions effective, our results
show that the ranking considers most important 1) the year difference between
publication and grant grant, 2) the amount of information provided in the
publication, and 3) the relevance of the publication to the grant. We discuss
future improvements of the system and an online tool for scientists to try.
