Premium
GotFunding: A grant recommendation system based on scientific articles
Author(s) -
Zeng Tong,
Acuna Daniel E.
Publication year - 2020
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.323
Subject(s) - relevance (law) , ranking (information retrieval) , matching (statistics) , computer science , process (computing) , work (physics) , library science , learning to rank , rank (graph theory) , information retrieval , recommender system , data science , political science , medicine , engineering , mathematics , law , mechanical engineering , pathology , operating system , combinatorics
Abstract 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 GotFunding (Grant recOmmendaTion based on past FUNDING), a recommendation system trained on National Institutes of Healthʼs (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 (a) the year difference between publication and grant, (b) the amount of information provided in the publication, and (c) the relevance of the publication to the grant. We further discuss the implications and future improvements to this work.