Knowledge-aware sequence modelling with deep learning for online course recommendation

作者：Deng, Weiwei; Zhu, Peihu; Chen, Han; Yuan, Tao; Wu, Ji^*

来源：INFORMATION PROCESSING & MANAGEMENT, 2023, 60(4): 103377.

DOI：10.1016/j.ipm.2023.103377

• Online course recommendation
• Massive online open courses
• Sequential recommendation
• Knowledge graph
• Deep learning

The recent boom in online courses has necessitated personalized online course recommendation. Modelling the learning sequences of users is key for course recommendation because the se-quences contain the dynamic learning interests of the users. However, current course recom-mendation methods ignore heterogeneous course information and collective sequential dependency between courses when modelling the learning sequences. We thus propose a novel online course recommendation method based on knowledge graph and deep learning which models course information via a course knowledge graph and represents courses using TransD. It then develops a bidirectional long short-term memory network, convolutional neural network, and multi-layer perceptron for learning sequence modelling and course recommendation. A public dataset called MOOCCube was used to evaluate the proposed method. Experimental results show that: (1) employing the course knowledge graph in learning sequence modelling improves averagely the performance of our method by 13.658%, 16.42%, and 15.39% in terms of HR@K, MRR@K, and NDCG@K; (2) modelling the collective sequential dependency improves averagely the performance by 4.11%, 6.37%, and 5.47% in terms of the above metrics; and (3) our method outperforms popular methods with the course knowledge graph in most cases.

• 单位
  Sun Yat-sen University