This paper proposes the new method which adopts the knowledge-based reasoning algorithms and collaborative filtering to create an e-learning material recommendation system. Major problems in recommendation system (RS) will be considered, including data preprocess, feature extraction, combination of knowledge-based reasoning and collaborative filtering algorithms, method of forming a weighted hybrid RS for better prediction. The experimental results show that our proposed method can achieve better prediction accuracy when comparing to rule-based reasoning (RBR), case-based reasoning (CBR), and Matrix Factorization (MF).
CITATION STYLE
Do, P., Nguyen, K., Vu, T. N., Dung, T. N., & Le, T. D. (2017). Integrating knowledge-based reasoning algorithms and collaborative filtering into e-learning material recommendation system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10646 LNCS, pp. 419–432). Springer Verlag. https://doi.org/10.1007/978-3-319-70004-5_30
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