Initialization of Matrix Factorization Methods for University Course Recommendations Using SimRank Similarities
Date Issued
2018-09-17
Author(s)
Krstova, Alisa
Stevanoski, Bozhidar
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.
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.
Subjects
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