Two-way Recommendation System for Supervisor Selection using Historical Data and Skyband-view Queries

Abstract

Completing the final project on time is one indicator of success in university. Unfortunately, many students have not been able to complete their studies on time. One of the factors that influence this is the failure in choosing the right supervisor. This study aims to create a recommendation system that can help students choose suitable supervisors. Unlike other studies, this research builds a two-way recommendation system that takes into account the preferences of students and supervisors. The previous study used the skyline views query concept to recommend dominant objects. However, the skyline view query concept has a major limitation: only skyline objects will be recommended to the user. Thus, students who are not skyline objects may not get a supervisor's recommendation, and vice versa. In this research, we use the concept of a skyband view query to overcome the limitation of the skyline view query. In addition to answering eight important queries from both parties, students and supervisors, the skyband view query concept is also able to overcome the shortcomings in previous research. Historical data from alumni is used to construct students' and supervisors' interests. This research succeeded in expanding the choice of research topics given in previous studies, as well as increasing the number of recommended supervisors and students.