Scholarly recommendation system for NIH funded grants based on biomedical word embedding models

Abstract

Objective:

Research grants, which are available from several sources, are essential for scholars to sustain a good standing in academia. Although securing grant funds for research is very competitive, being able to locate and find previously funded grants and projects that are relevant to researchers’ interests would be very helpful. In this work, we developed a funded-grants/projects recommendation system for the National Institute of Health (NIH) grants.

Methods:

Our system aims to recommend funded grants to researchers based on their publications or input keywords. By extracting summary information from funded grants and their associated applications, we employed two embedding models for biomedical words and sentences (biowordvec and biosentvec), and compare multiple recommendation methods to recommend the most relevant funded grants for researchers’ input

Results:

Compared to a baseline method, the recommendation system based on biomedical word embedding models provided higher performance. The system also received an average rate of 3.53 out of 5, based on the relevancy evaluation results from biomedical researchers.

Conclusion:

Both internal and external evaluation results prove the effectiveness of our recommendation system. The system would be helpful for biomedical researchers to locate and find previously funded grants related to their interests.