Knowledge-aware sequence modelling with deep learning for online course recommendation |
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| Institution: | 1. School of Economics and Management, South China Normal University, Guangzhou, China;2. Department of Information Systems, City University of Hong Kong, Hong Kong SAR, China;3. College of Teacher Education, South China Normal University, Guangzhou, China;4. Business School, Sun Yat-sen University, Guangzhou, China;1. Smart Tourism Eudcation Platform (STEP), College of Hotel and Tourism Management, Kyung Hee University, South Korea;2. Department of Tourism, Hospitality and Event Management, University of Florida, USA;3. Information Systems Institute, Leipzig University, Germany |
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| Abstract: | 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 sequences contain the dynamic learning interests of the users. However, current course recommendation 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. |
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