.png){kind=icon}
返回
Knowledge-aware sequence modelling with deep learning for online course recommendation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ipm.2023.103377.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Online course recommendation
Massive online open courses
Sequential recommendation
Knowledge graph
Deep learning
期刊
I
IF:
6.9
论文数:
5.2K
被引数:
1.4W
机构
引用论文
A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph知识图谱视角下推荐系统研究热点与前沿趋势综述
OAAI
OAAI分享收藏ABiNE-CRS: course recommender system in online education using attributed bipartite network embedding.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏Hotness prediction of scientific topics based on a bibliographic knowledge graph基于书目知识图谱的科学主题热度预测
PREAI
分享收藏
查看更多内容