Affiliations: School of Marxism, China University of Geosciences, Wuhan, Hubei, China
Correspondence:
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Corresponding author. Xiangfei Ma, China University of Geosciences, Wuhan, Hubei, China. E-mail: [email protected].
Abstract: The sustainable economic learning course recommendation can quickly find the knowledge information that the user really needs from the massive information space and realize the personalized recommendation to the user. However, the occurrence of trust attacks seriously affects the normal recommendation function of the recommendation system, resulting in its failure to provide users with reliable and reliable recommendation results. In order to solve the vulnerability of the recommendation system to the support attack, based on text vector model and support vector machine, this paper makes a comprehensive analysis of the current research status of the robust recommendation technology. Moreover, based on the idea of suspicious user metrics, this paper has conducts in-depth research on how to design highly robust recommendation algorithms, and constructs a highly reliable sustainable economic learning course recommendation model. In addition to this, this research tests the performance of the system from two perspectives of course recommendation satisfaction and system retrieval accuracy. The experiment proves that the model constructed in this paper performs well in the recommendation of sustainable economic learning courses.
Keywords: Text vector model, support vector machine, sustainable economy, system construction
