Method of personalized educational resource recommendation based on LDA and learner’s behavior

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Abstract
With the popularization of online education and the rapid increase of network learning resources, it is urgent to provide high-quality personalized resource recommendation services for learners. Collaborative filtering recommendation is one of the most commonly used recommendation methods. This paper calculates the importance of courses by analyzing the external attribute tolerance and internal attribute quality value, and then constructs LDA user interest model to calculate the user's preference for topics, so as to complete the recommendation of personalized learning resource. The experimental results show that the proposed method can effectively improve the effect of personalized learning resource recommendation. The proposed algorithm has higher accuracy and effectively improves the management efficiency of online course resources. The proposed method provides the reference to the personalized educational resource recommendation.
Funding Information
  • the Innovation Team of Guangdong Provincial Department of Education (2018KCXTD031)
  • MOE (Ministry of Education in China) Liberal Arts and Social Sciences Foundation and Pengcheng Scholar Funded Scheme (Nos. 17YJCZH157)
  • Foundation for the Author of National Excellent Doctoral Dissertation of China (2014-92)

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