Title: An online ideological and political courses recommended method in colleges and universities based on weighted collaborative filtering algorithm

Authors: Yun Peng; Xue Wang

Addresses: School of Music and Dance, Hunan City University, YiYang, 413000, China ' Foreign Language Department, Shanghai Xingjian College, Shanghai, 200072, China

Abstract: In order to reduce the error of course recommendation and increase the duration of online learning for students, this paper proposes a university ideological and political online course recommendation method based on weighted collaborative filtering. Firstly, the mean centred method is used to standardise user ratings; user similarity is calculated based on interest and trust. Secondly, constructing a user tag weight matrix and a course tag weight matrix helps better describe user needs. Finally, after calculating the label weights, based on the idea of collaborative filtering, the weighted generalised Mahalanobis distance is used to calculate the closeness of the recommendation scheme; the Top-N closest recommendation scheme is selected to recommend to learners. The experimental results show that this method significantly reduces course recommendation errors and improves the online learning duration of students, with a minimum recommendation error of only 0.09.

Keywords: weighted collaborative filtering; ideological and political online courses; course recommendation; label weight.

DOI: 10.1504/IJBIDM.2025.143933

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.1/2, pp.190 - 204

Received: 18 Dec 2023
Accepted: 31 May 2024
Published online: 14 Jan 2025 *

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