Collaborative Filtering based Course Recommender using OWA operators

Recommendation systems guide users to choose the most appropriate items among numerous alternatives based on predicting their interests. Recently, it is seen that recommendation systems have become to be widely used in educational domain, especially in course recommender applications. The objectives of these systems is facilitating course selection process of students and reducing their stresses. The current course recommendation studies generally consider the most recent grades of the courses taken by students and ignore the case of repeating the course under the pass-fail or grade replacement options. However, retaking a course is the primary parameter giving opinion about tendency of the students to the courses. In this study, we propose a novel collaborative filtering (CF) based course recommendation system considering the case of repeating a course and students' grades in the course for each repetition. We experiment different Ordered Weighted Averaging (OWA) operators which aggregates grades for each student's repeated courses to enhance the recommendation quality. The normalized mean absolute error (MAE) of our approach using CF and OWA is calculated as 0,063 which is encouraging for future work.