Title: Real-time recommendation method of online education resources based on improved decision tree algorithm

Authors: Shufang Xiao; Jue Liu; Ping Xu

Addresses: Guizhou Vocational Technical College of Electronic & Information, Guizhou 556000, China ' International Design School for Advanced Studies, Hongik University, Seoul, 03082, South Korea ' Faculty of Arts and Design, Shanwei Polytechnic, Guangdong 516600, China

Abstract: In order to improve the accuracy of online education resources recommendation and reduce the recommendation time, this paper introduces the improved decision tree algorithm to design a real-time recommendation method of online education resources. We calculate the information gain rate of online educational resources and collect the dataset of educational resources to obtain the attribute information of online educational resources. The specific attribute branches of users are matched according to the information gain rate, and the pruning structure of incomplete data feature attributes of online education resources is constructed by discretisation method. C4.5 algorithm is used to improve the discreteness of decision tree algorithm, and the improved decision tree is used to classify online education resources. The real-time path of online resource recommendation is simulated to realise real-time resource recommendation. The results show that this method can improve the real-time recommendation effect of educational resources.

Keywords: improved decision tree algorithm; C4.5 algorithm; data entropy; knowledge association map.

DOI: 10.1504/IJICT.2023.134840

International Journal of Information and Communication Technology, 2023 Vol.23 No.4, pp.388 - 400

Received: 23 Jul 2021
Accepted: 01 Oct 2021
Published online: 14 Nov 2023 *

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