GotFunding: A grant recommendation system based on scientific articles
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917672444231680 |
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| author | Zeng, Tong Acuna, Daniel E. |
| author_facet | Zeng, Tong Acuna, Daniel E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12840 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GotFunding: A grant recommendation system based on scientific articles Zeng, Tong Acuna, Daniel E. Information Retrieval Digital Libraries Machine Learning 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. |
| title | GotFunding: A grant recommendation system based on scientific articles |
| topic | Information Retrieval Digital Libraries Machine Learning |
| url | https://arxiv.org/abs/2405.12840 |