Title: New media popular music recommendation system based on machine learning algorithm

Authors: Zijin Wu; Sri Azra Attan; Wuxiang Shi; Lei Jiao; Qi He

Addresses: College of Humanities and Management, Guilin Medical University, Guilin 541199, Guangxi, China; Faculty of Social Sciences and Liberal Art, UCSI University, Kuala Lumpur 56000, Malaysia ' Faculty of Social Sciences and Liberal Art, UCSI University, Kuala Lumpur 56000, Malaysia ' College of Humanities and Management, Guilin Medical University, Guilin 541199, Guangxi, China ' Guangxi Normal University Committee of the Communist Youth League, Guangxi Normal University, Guilin 541006, Guangxi, China ' College of Art, Hubei University of Education, Wuhan 430205, Hubei, China

Abstract: With the development of new media and AI, music recommendation systems have become essential in meeting diverse user preferences. Traditional collaborative filtering methods neglect music content, leading to less accurate recommendations. This paper proposes a machine learning-based popular music recommendation system that addresses these limitations. By analysing user preferences and applying the maximum entropy model (MEM), the system integrates and processes music data more effectively. Experimental results show that the new approach enhances recommendation accuracy by 7.5%, making the system more intelligent and user-friendly. This study contributes to improving music recommendation performance in the era of new media.

Keywords: music recommendation system; popular music; collaborative filtering; machine learning algorithm; CTR estimation; maximum entropy model; MEM.

DOI: 10.1504/IJART.2025.147851

International Journal of Arts and Technology, 2025 Vol.15 No.3, pp.283 - 307

Received: 20 Mar 2025
Accepted: 24 Apr 2025
Published online: 04 Aug 2025 *