Recommender Systems for university elective course recommendation

Abstract

Recommender Systems are an ongoing research that is applied in various domains. Course recommendation is considered a challenged domain that has not been explored thoroughly. It benefits undergraduate students who need suggestion and also enhances course selection processes during the pre-registration period. This paper introduces a recommendation system for university elective courses, which recommends the courses based on the similarity between the course templates of students. This paper utilizes two popular algorithms: collaborative based recommendation using Pearson Correlation Coefficient and Alternating Least Square (ALS), and compares their performance on a dataset of academic records of university students. The experimental results show that applying ALS in this domain is superior to collaborative based with 86 percent of accuracy.