Edge-cloud computing oriented large-scale online music education mechanism driven by neural networks

With the advent of the big data era, edge cloud computing has developed rapidly. In this era of popular digital music, various technologies have brought great convenience to online music education. But vast databases of digital music prevent educators from making specific-purpose choices. Music reco...

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Bibliographic Details
Published in:Journal of cloud computing : advances, systems and applications systems and applications, 2024-12, Vol.13 (1), p.55-10, Article 55
Main Authors: Xing, Wen, Slowik, Adam, Peter, J. Dinesh
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Cloud computing
Computer Communication Networks
Computer Science
Computer System Implementation
Computer Systems Organization and Communication Networks
Data integration
Digital music
Edge computing
Edge-cloud computing
Education
Information Systems Applications (incl.Internet)
Large-scale information fusion
Machine learning
Mathematical analysis
Mobile Edge Computing Meets AI
Music education
Music recommendation
Neural networks
Recommender systems
Software Engineering/Programming and Operating Systems
Special Purpose and Application-Based Systems
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Summary:With the advent of the big data era, edge cloud computing has developed rapidly. In this era of popular digital music, various technologies have brought great convenience to online music education. But vast databases of digital music prevent educators from making specific-purpose choices. Music recommendation will be a potential development direction for online music education. In this paper, we propose a deep learning model based on multi-source information fusion for music recommendation under the scenario of edge-cloud computing. First, we use the music latent factor vector obtained by the Weighted Matrix Factorization (WMF) algorithm as the ground truth. Second, we build a neural network model to fuse multiple sources of music information, including music spectrum extracted from extra music information to predict the latent spatial features of music. Finally, we predict the user’s preference for music through the inner product of the user vector and the music vector for recommendation. Experimental results on public datasets and real music data collected by edge devices demonstrate the effectiveness of the proposed method in music recommendation.
ISSN:2192-113X
2192-113X
DOI:10.1186/s13677-023-00555-y