Dynamic Online Course Recommendation Based on Course Network and User Network

  • Xixi Yang
  • Wenjun JiangEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)


E-learning attracts much attentions and gains sustainable development in recent years. Course recommendation tries to recommend proper courses to users from a large number of online courses. Existing works usually focus on improving the accuracy, neglecting to match the recommended course with user’s knowledge level. It results in a high enrollment rate but low grades, indicating poor learning results. Moreover, course recommendation also faces the challenges of sparse user-rating matrix and sparse social learning network. In this paper, we try to recommend courses that are fit to user’s knowledge level. To this end, we (1) propose to construct social learning network, for which we first build the user network and the course network, and combine them together; (2) explore the social learning network to extend the user-rating matrix by HITS algorithm, so as to overcome the sparsity challenge; (3) sort the recommendation list to meet user’s knowledge level, exploiting the course network. Experiments in a real e-learning dataset show that our model performs well in online course recommendation, and the learning results are better, validating the effectiveness of considering user’s knowledge level.


User network Course network User-rating matrix User’s knowledge level Dynamic course recommendation 



This research was supported by NSFC grant 61632009 and Outstanding Young Talents Training Program in Hunan University 531118040173.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Computer Science and Electronic EngineeringHunan UniversityChangshaChina

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