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

Authors: Xixi Zhang

Addresses: Changchun University of Architecture and Civil Engineering, Changchun, 13000, China

Abstract: 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.

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

DOI: 10.1504/IJBIDM.2024.137738

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.3/4, pp.379 - 393

Received: 29 Nov 2022
Accepted: 23 Mar 2023
Published online: 04 Apr 2024 *

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