Online Learning of Social Science Courses Based on Personalized Recommendation Algorithm

Authors

  • Rong Xie Ningbo City College of Vocational Technology, Ningbo, Zhejiang 315100, China

Abstract

The recommendation algorithm has been used to find the course of interest in the vast amount of online learning resources. This paper analyzed the recommendation algorithm briefly and designed an improved user-based collaborative filtering recommendation algorithm (UCFRA) recommendation algorithm based on UCFRA by improving its matrix filling based on the item attribute. It was found that when the number of K nearest neighbors was 80, the precision rate of the improved UCFRA was 0.5387 for the MovieLens 100K dataset, which was 5.13% higher than that of the UCFRA. For the social science course dataset, the highest precision rate of the improved UCFRA was 0.5429, and the highest coverage rate was 0.4559, both of which
were better than the UCFRA. The experimental results verified that the reliability of the designed recommendation algorithm for social science course recommendation. The algorithm can be further applied.

Keywords: recommendation algorithm, social science course, online learning, collaborative filtering

Cite As

R. Xie, "Online Learning of Social Science Courses Based on Personalized Recommendation Algorithm",
Engineering Intelligent Systems, vol. 31 no. 6, pp. 467-472 , 2023.



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Published

2023-11-01

Issue

Vol. 31 No. 6 (2023): International Journal of Engineering Intelligent Systems

Section

Article

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