Recommender Systems for university elective course recommendation
A recommendation system for university elective courses, which recommends the courses based on the similarity between the course templates of students is introduced, which shows that applying ALS in this domain is superior to collaborative based with 86 percent of accuracy.
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.