Title: Building a collaborative university teaching platform based on online social space

Authors: Xiaomei Zhang

Addresses: Marxist Academy, Henan Polytechnic Institute, Nanyang, 473000, China

Abstract: To recommend learning resources that are suitable for students' learning development, a content recommendation algorithm and KRCF algorithm are proposed based on the relationship between students and knowledge resources, as well as knowledge similarity user groups, to correct and predict students' learning paths, and to establish a university teaching collaboration platform. The experimental results show that the KRCF algorithm performs better than algorithms such as the CF algorithm. Under different iterations or training set ratios, its RSME and MAE values are the smallest. When the training set ratio is 0.15, its MAE value is 0.72, which is lower than the MCS-CF algorithm. Overall, the accuracy of the KRCF algorithm is over 70%, with an accuracy of 94.67% when there are 50 neighbours. The accuracy of content recommendation algorithms in recommending learning resources is high, and personalised systems can quickly respond to user requests.

Keywords: online social space; content recommendation algorithm; knowledge association; collaborative filtering recommendation algorithm.

DOI: 10.1504/IJCSYSE.2024.142763

International Journal of Computational Systems Engineering, 2024 Vol.8 No.3/4, pp.229 - 237

Received: 23 Mar 2023
Accepted: 06 Jun 2023
Published online: 21 Nov 2024 *

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