Strengthening research partner collaboration in higher education for searching innovation through machine learning-based recommender system

Abstract

Academic collaboration is tremendously important for higher education. Multidisciplinary academicians may be grouped as a better research collaboration than the previous one. Therefore, such system is needed, even for a huge number of academicians in a institution. However, existing such recommendation tools are expensive. This paper suggests to develop a system by using machine learning approach in order to search a big academicians data effectively. Hence, with help of standard of Naïve Bayes creates a flexible text search without depending on what select options including research location or case study instead of only research topic. Furthermore, the output of Naïve Bayes, then, is tranformed to percentage display in order to bring ease of understanding the gap of recommendation. It allows the user to choose a possible partner more than one. Therefore, this approach helps reduce time and effort.