Title: A personalised recommendation method of online educational resources on social media platform

Authors: Ziqian Xu

Addresses: Huai'an Campus of Nanjing Forestry University, Nanjing 210037, JiangSu, China

Abstract: Aiming at the problems of low recommendation accuracy and low user preference in traditional methods, a personalised recommendation method of online educational resources on social media platform is proposed. Firstly, the crawler technology is used to obtain the online educational resources data, and the resource data features are extracted. Then, the similarity of data features is calculated through cosine similarity, and the feature data with high similarity is fused to complete the feature preprocessing of educational resources data. Finally, the user's demand for resource data and preference degree are determined through the user interest model, so as to construct the online educational resources personalised recommendation model, and take the educational resources data and user preference degree as the input data to complete the educational resources personalised recommendation. The experimental results show that the proposed method has high accuracy and user preference.

Keywords: social media platform; online educational resources; personalised recommendation; reptile technology; interest model.

DOI: 10.1504/IJWBC.2023.131379

International Journal of Web Based Communities, 2023 Vol.19 No.2/3, pp.123 - 136

Received: 19 Oct 2021
Accepted: 21 Feb 2022
Published online: 09 Jun 2023 *

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