Title: A study of personalised recommendation methods for multimedia ELT online course

Authors: Siyi Chen; Xinli Ke; Xiaohong Zhang

Addresses: Department of Foreign Languages, Southwest Jiaotong University Hope College, Jintang, 610400, China ' Department of Foreign Languages, Southwest Jiaotong University Hope College, Jintang, 610400, China ' School of Foreign Languages and Cultures, Panzhihua University, Panzhihua, 617000, China

Abstract: Aiming at the problem that a large number of online courses lead to the reduction of students' efficiency in finding suitable courses, the collaborative filtering recommendation algorithm is improved. The 'user project' scoring matrix is used to calculate the reasonable scoring factors. At the same time, the 'user project' scoring matrix and project characteristics are used to establish a composite feature matrix. Then, combined with demographic information, a mixed user model is established to obtain a neighbourhood set close to the real situation, and finally the best recommendation result is generated. The improved hybrid user model collaborative filtering algorithm (IHUMCF) is used for personalised recommendation. Compare IHUMCF with HUMCF and UBCF. The results showed that IHUMCF recommended the most in the same time; IHUMCF has the highest accuracy rate, recall rate and comprehensive evaluation index, and the lowest average error. It shows that collaborative filtering recommendation algorithm based on improved hybrid user model can improve the accuracy of personalised recommendation, provide better recommendation effect, and analyse students' potential learning needs to provide students with a better online learning environment.

Keywords: collaborative filtering algorithm; multimedia English teaching; online courses; personalised recommendation.

DOI: 10.1504/IJCSYSE.2024.137449

International Journal of Computational Systems Engineering, 2024 Vol.8 No.1/2, pp.96 - 106

Received: 02 Aug 2022
Accepted: 21 Oct 2022
Published online: 19 Mar 2024 *

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