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Context-Aware Network Analysis of Music Streaming Services for Popularity Estimation of Artists

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Title: Context-Aware Network Analysis of Music Streaming Services for Popularity Estimation of Artists
Authors: Matsumoto, Yui Browse this author
Harakawa, Ryosuke Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Metadata
Estimation
Feature extraction
Biographies
Videos
Visualization
Social network services
Music
social network services
complex networks
prediction algorithms
classification algorithms
Issue Date: 4-Mar-2020
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: IEEE Access
Volume: 8
Start Page: 48673
End Page: 48685
Publisher DOI: 10.1109/ACCESS.2020.2978281
Abstract: A novel trial for estimating popularity of artists in music streaming services (MSS) is presented in this paper. The main contribution of this paper is to improve extensibility for using multi-modal features to accurately analyze latent relationships between artists. In the proposed method, a novel framework to construct a network is derived by collaboratively using social metadata and multi-modal features via canonical correlation analysis. Different from conventional methods that do not use multi-modal features, the proposed method can construct a network that can capture social metadata and multi-modal features, <italic>i.e.</italic>, a context-aware network. For effectively analyzing the context-aware network, a novel framework to realize popularity estimation of artists is developed based on network analysis. The proposed method enables effective utilization of the network structure by extracting node features via a node embedding algorithm. By constructing an estimator that can distinguish differences between the node features, the proposed method can archive accurate popularity estimation of artists. Experimental results using multiple real-world datasets that contain artists in various genres in Spotify, one of the largest MSS, are presented. Quantitative and qualitative evaluations show that our method is effective for both classifying and regressing the popularity.
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: article
URI: http://hdl.handle.net/2115/78131
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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