A Score Prediction Approach for Optional Course Recommendation via Cross-User-Domain Collaborative Filtering

Optional course selection is a critical activity for college students due to a large number of available but unfamiliar optional courses. Improper selection of optional courses would seriously affect the students' optional course achievements, which enforces students to drop out the improperly selected optional courses. Therefore, there is an urgent need to develop an optional course recommendation system. In this paper, we develop an optional course recommendation system based on score prediction. In particular, a novel cross-user-domain collaborative filtering algorithm is designed to accurately predict the score of the optional course for each student by using the course score distribution of the most similar senior students. After generating the predicted scores of all optional courses, the top $t$ optional courses with the highest predicted scores without time conflict will be recommended to the student. The extensive experiments have been conducted to evaluate the effectiveness of the proposed method, and the results show that the proposed method is able to accurately recommend optional courses to students who will achieve relatively high scores.