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.

翻译：获取研究经费是成为成功科学家的重要环节。青年学者需耗费大量精力寻找最能匹配其研究背景的资助机构和项目。但影响论文-基金最佳匹配的因素究竟是什么？部分高校可能聘用预审人员来分析这些因素，然而并非所有机构都负担得起这类人员配置。由基金资助发表的论文历史记录能帮助我们理解匹配机制，并推动开发自动化推荐系统。本研究提出 \textsc{GotFunding}（基于历史资助的基金推荐系统），该推荐系统基于美国国立卫生研究院（NIH）的基金-论文关联数据集进行训练。通过将问题转化为排序学习框架，系统实现了高性能（NDCG@1 = 0.945）。通过分析预测有效性的关键特征，结果表明排序算法最看重以下三个因素：1）论文发表与基金获批的时间差；2）论文信息丰度；3）论文与基金主题的相关性。本文还讨论了系统优化方向及供科学家在线使用的工具。