Personalised recommendation method of online ideological and political education resources in colleges and universities based on spectral clustering

被引：0

作者：

Zhang, Xixi ^{[1
]}

机构：

[1] Changchun University of Architecture and Civil Engineering, Changchun

来源：

International Journal of Business Intelligence and Data Mining | 2024年 / 24卷 / 3-4期

关键词：

ideological and political education; minimum cut set; objective function; online education resources; personalised recommendation; spectral clustering;

D O I：

10.1504/IJBIDM.2024.137738

中图分类号：

TN911 [通信理论];

学科分类号：

081002 ;

摘要：

Due to the problems of low accuracy and low F-value in online educational resource recommendation using existing methods, a personalised recommendation method for online educational resource recommendations based on spectral clustering is proposed for ideological and political teaching in universities. First, Pearson formula was used to determine the degree of similarity and collect data from hidden online ideological and political education resources. Then, Kalman filtering algorithm is used to preprocess the collected relevant resource data. Finally, the spectral clustering algorithm is used to determine the data distance of resources according to the neighbour relationship, so as to make the recommended resources personalised, so as to build the personalised recommendation model for online educational resources, and realise the recommendation of resources. The experimental results show that the F-value of the proposed method is about 1.0 and the paste progress and recommendation accuracy are both higher than 95%, which are 10% and 12% higher than the comparison method respectively, which verifies the good recommendation effect of this method. Copyright © 2024 Inderscience Enterprises Ltd.

引用

页码：379 / 393

页数：14

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