Abstract
In order to enhance the personalized recommendation effect of Korean online education course resources and improve the accuracy of recommendations. This article designs a Korean online education course resource recommendation method based on user preference model. The Korean online education curriculum data is extracted by HtmlParser, the Korean online education curriculum text data is classified by Text segmentation, the Korean online education curriculum resource characteristics are obtained by the subjective clause extraction method, the Korean online education curriculum user preference model is constructed according to the user evaluation information, and the Korean online education curriculum resource recommendation is realized according to the Collaborative filtering recommendation algorithm. The experimental results show that the average recommendation accuracy of this method reaches 16.702% and effectively improves the recall rate.
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Shi, S. (2024). Korean Online Education Curriculum Resources Recommendation Method Based on User Preference Model. In: Gui, G., Li, Y., Lin, Y. (eds) e-Learning, e-Education, and Online Training. eLEOT 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-031-51471-5_27
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DOI: https://doi.org/10.1007/978-3-031-51471-5_27
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