GotFunding：基于科学论文的基金推荐系统
GotFunding: A grant recommendation system based on scientific articles
AI总结：
本文提出 GotFunding，基于 NIH 基金—论文记录并以排序学习实现基金推荐，NDCG@1 达 0.945，同时揭示年份差、论文信息量和相关性是关键因素。
摘要：
获得资助是成为成功科学家的重要组成部分。初级教员要花费大量时间寻找与其研究概况最匹配的合适机构和项目。但是，影响最佳论文—基金匹配的因素有哪些？一些大学可能会雇用资助前工作人员来理解这些因素，但并非所有机构都有能力聘请他们。由基金资助的论文历史记录可以帮助我们理解匹配过程，也有助于开发推荐系统来实现该过程的自动化。在本工作中，我们提出了 GotFunding（Grant recOmmendaTion based on past FUNDING，基于过往资助的基金推荐），这是一个基于美国国立卫生研究院（National Institutes of Health，NIH）的基金—论文记录训练的推荐系统。通过将该问题转化为排序学习，我们的系统取得了高性能（NDCG@1 = 0.945）。通过分析使预测有效的特征，我们的结果表明，排序最看重：1）论文与基金之间的年份差，2）论文所提供的信息量，以及3）论文与基金的相关性。我们讨论了该系统未来的改进，以及一个供科学家试用的在线工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
