An Intelligent Recommendation Service for Student-Selection on Research Social Network: Bridging the Gap Between Students and Supervisors—Research-in-Progress

Abstract

Student-selection is of critical importance for research supervisors in the higher education environment. On account of the information asymmetry, it poses a significant challenge for supervisors to find the most appropriate students. Current studies are limited to the context of one university. They are not suitable in web 2.0 era which are inundated with vast online information. This article proposes an intelligent approach with the help of recommendation system techniques which have emerged in both research social network to provide services for scholars and e-commerce applications to serve for consumers. Furthermore, the method distinguishes a supervisor according to his co-author network firstly. Then, it applies respective recommendation strategy to provide student recommendation services for the target supervisor. A prototype is implemented on Scholarmate, which is a research social network website emphasizing communication between students and supervisors. The evaluation of our proposed method will be completed in the future.