A Recommendation System of Grants to Acquire External Funds

TL;DR

This paper presents an improved grant recommendation system for university researchers that uses association rules and web mining to better match researchers with suitable grants, enhancing accuracy by incorporating past records and publications.

Contribution

The paper introduces modifications to an existing system, extending data retrieval methods and integrating researcher publication data to improve grant recommendation accuracy.

Findings

Enhanced recommendation accuracy through data extension

Successful integration of researcher publication data

Positive simulation results demonstrating system improvements

Abstract

The recommendation system of the competitive grants to university researchers by using the Grants-in-Aid for Scientific Research (KAKEN) keywords has been developed. The system can determine the recommendation order of researchers to each grant by the using the association rules between KAKEN application and various information from the web site of the corresponding grant. However, our developed previous system has some fatal errors in the retrieval algorithm. We modify the algorithm and extend the retrieval data for web mining. If the grant information is not enough to determine the relation, the system investigates the past KAKEN records in the database for the researcher who acquired the past grant. Moreover, the system retrieves the papers of the researchers to search their interests. As a result, the agreement degree of the researcher's interest to the grant increases. This paper discusses some simulation results.