Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners' rating
Rudolf Turnip, Dade Nurjanah, Dana Sulistyo Kusumo · 2017
Web-based learning environment has led to the creation of a big number of digital learning materials that offers various topics. Learners spend much time for browsing and filtering information that suits their needs. Limited time can prevent learners from finding useful learning materials. One of the most successful such technique to solve the problem is recommender system which can select items the user is interested in from a large amount of data. Previous research has attempted to use content-based filtering combined with good learners' ratings method (CBF-GL method). This research proposes an improved method for an existing e-learning recommender system with combination of content-based filtering and collaborative filtering with good learners' ratings (CBF-CF-GL method). Adding a collaborative filtering method intended that only ratings from good learners with certain similarity with the active learner are used in rating recommendations. Experiments were conducted for measuring and comparing the Mean Absolute Error (MAE) scores of CBF-CF-GL and CBF-GL methods using the same dataset. The experiment shows that the CBF-GL method has produced a MAE score of 0.542, while the CBF-CF-GL method has given a MAE score of 0.447. It has shown that the inclusion of collaborative filtering considering good learners' ratings has improved the recommendation accuracy.