Combining User-Driven Social Marketing with System-Driven Personalized Recommendation for Student Finding

Student selection is of crucial importance for supervisors who are choosing students for postgraduate studies or research projects.Due to the challenge of asymmetric information,it is difficult for them to find suitable candidates.The existing methods do not work so well in the web 2.0 context which is inundated with vast online information.In order to overcome the deficiency,a research social network enhanced approach is proposed to provide decision support.It appeals to supervisors to adopt the proposed user-driven social marketing strategy.Meanwhile,this study mainly presents a system-driven personalized recommendation approach to support supervisors'decisions of student selection.The proposed method distinguishes supervisors based on their co-author networks to extract their potential preferences of collaboration styles.Subsequently,corresponding recommendation strategies are employed to provide personalized student recommendation services for targeted supervisors.A prototype is implemented on ScholarMate which provides online communication channels for researchers.A user study is conducted to verify the effectiveness of the proposed approach.The results enlighten designers to consider the differences among different users when designing recommendation strategies.