Title: Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing
Authors: Juan Xia
Addresses: College of Music, Hubei Normal University, Huangshi, Hubei, China
Abstract: The purpose is to better develop China's music industry, facilitate users' online music works queries, and encourage the development of excellent music works. The music recommendation system is analysed, and a new music recommendation system is constructed based on the combination of the hybrid Deep Learning (DL) Artificial Neural Network (ANN) and Mobile Edge Computing (MEC) technologies. First, the principle of MEC is analysed. Then, the combination of Deep Learning Artificial Neural Network (DLANN) technology and MEC technology is discussed. Afterward, DLANN and MEC are fused to implement the music recommendation model, and the proposed model is evaluated through experiment. The results show that the combination of DLANN and MEC technologies can increase the DL efficiency of the computer, the storage capacity, and the overall efficiency of the servers. This proves the feasibility of the proposed music recommendation system. The user satisfaction of the proposed music recommendation system exceeds all mainstream music recommendation algorithms on the market. Thus, the research can provide a reference for the improvement of the music recommendation algorithm in the future and is of great significance. The proposed music recommendation system outperforms most of the current music recommendation systems on the market.
Keywords: deep learning; artificial neural network; mobile edge computing; music recommendation model.
DOI: 10.1504/IJGUC.2022.124405
International Journal of Grid and Utility Computing, 2022 Vol.13 No.2/3, pp.183 - 194
Received: 20 May 2021
Accepted: 12 Sep 2021
Published online: 26 Jul 2022 *