Title: Study of online learning resource recommendation based on improved BP neural network

Authors: Yonghui Dai; Jing Xu

Addresses: Management School, Shanghai University of International Business and Economics, Shanghai, 201620, China ' School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China

Abstract: Personalised recommendation has gradually become an effective way to solve the problem of information overload in the era of big data. Therefore, in order to improve the efficiency of online learning, this paper discusses the design of online learning resource recommendation algorithm based on improved BP neural network, and the results show that it has high value for popularisation and application. Based on the transmission network, the improved BP neural network of momentum factor can achieve more efficient data mining. After training learning resources and user data, it can match the real score and the predicted score, so as to ensure the accuracy of personalised recommendation. The main contribution of this paper is to propose a recommendation algorithm to online learning resources through improved BP neural network algorithm, and the feasibility of the algorithm is verified. The research method of this paper provides a reference for the research of personalised recommendation algorithm of online resources.

Keywords: online learning resource; improved BP neural network; personalised recommendation algorithm; momentum factor.

DOI: 10.1504/IJES.2021.113834

International Journal of Embedded Systems, 2021 Vol.14 No.2, pp.101 - 107

Received: 03 Jun 2020
Accepted: 15 Jun 2020
Published online: 31 Mar 2021 *

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