Initialization of Matrix Factorization Methods for University Course Recommendations Using SimRank Similarities

Abstract

The accurate estimation of students’ grades in prospective courses is important as it can support the procedure of making an informed choice concerning the selection of next semester courses. As a consequence, the process of creating personal academic pathways is facilitated. This paper provides a comparison of several models for future course grade prediction based on three matrix factorization methods. We attempt to improve the existing techniques by combining matrix factorization with prior knowledge about the similarity between students and courses calculated using the SimRank algorithm. The evaluation of the proposed models is conducted on an internal dataset of anonymized student record data.