Title: A personalised recommendation algorithm of ideological and political education resources based on hybrid collaborative filtering

Authors: Shan Zhang

Addresses: College of Marxism, Hunan City University, Yiyang, 413000, Hunan, China

Abstract: In order to overcome the problems of large error and long time consuming of existing recommendation methods, this paper proposes a personalised recommendation algorithm of ideological and political education resources based on hybrid collaborative filtering. First, with the help of Oracle, collect ideological and political education resources data to complete the data extraction of ideological and political education resources. Secondly, the data of ideological and political education resources are classified, data removal and data dimension reduction pre-treatment. Finally, the recommended user-resource evaluation matrix of ideological and political education resources is constructed to determine the similarity of the recommended objects of ideological and political education resources. Hybrid collaborative filtering algorithm is introduced to build a personalised recommendation model and realise the personalised recommendation. The experimental results show that the method can reduce the recommendation error and shorten the recommendation time, with the highest recommendation error exceeding 0.03%.

Keywords: hybrid collaborative filtering; ideological and political education resource data; Oracle; personalised recommendation algorithm.

DOI: 10.1504/IJRIS.2025.146928

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.2, pp.107 - 113

Received: 09 Feb 2023
Accepted: 15 May 2023
Published online: 27 Jun 2025 *

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