Title: Big data analysis for sustainable music teaching using collaborative filtering recommendation optimisation algorithm

Authors: Lanqian Liu

Addresses: Department of Music and Dance, Hunan University of Science and Engineering, Yongzhou, 425000, China

Abstract: The use of big data analysis technology to meet the needs of sustainable development of music teaching has become a current research hotspot. In order to improve the teaching effect of students and teachers, this study first proposed the K-means clustering algorithm (BSLCSK means) based on two-way selective learning chicken flock optimisation (BSLCS), and then designed the improved cooperative filtering algorithm (BSLCSIUCF) integrating BSLCSK means. The results show that the maximum and minimum mean absolute errors (MAE) of the BSLCSIUCF algorithm are 1.1 and 0.4, respectively. When the number of neighbours is 10 and 20, the MAE values of the algorithm are 1.1137 and 0.9144, respectively. The accuracy of prediction and recommendation is further improved. While obtaining more accurate recommendation results, the recommendation results obtained are also more stable, providing better music big data teaching services for college teachers and students.

Keywords: K-means clustering; chicken swarm algorithm; collaborative filtering algorithm; music; big data.

DOI: 10.1504/IJCSYSE.2024.142772

International Journal of Computational Systems Engineering, 2024 Vol.8 No.3/4, pp.273 - 282

Received: 24 Mar 2023
Accepted: 08 Jun 2023
Published online: 21 Nov 2024 *

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