Context-Aware Network Analysis of Music Streaming Services for Popularity Estimation of Artists
Context-Aware Network Analysis of Music Streaming Services for Popularity Estimation of Artists
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DOI：
10.1109/access.2020.2978281
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发表时间：
2020-03
期刊：
影响因子：
3.9
通讯作者：
Yui Matsumoto;Ryosuke Harakawa;Takahiro Ogawa;M. Haseyama
中科院分区：
文献类型：
--
作者：
Yui Matsumoto;Ryosuke Harakawa;Takahiro Ogawa;M. Haseyama
本文提出了一种估算音乐流媒体服务（MSS）中艺术家受欢迎程度的新方法。本文的主要贡献是提高了使用多模态特征来准确分析艺术家之间潜在关系的可扩展性。该方法通过典型相关分析，将社会元数据和多模态特征协同使用，推导出一种新的网络构建框架。与不使用多模态特征的传统方法不同，该方法可以构建一个能够捕获社会元数据和多模态特征的网络，即上下文感知网络。为了有效地分析情境感知网络，提出了一种基于网络分析的艺术家人气评估框架。该方法通过节点嵌入算法提取节点特征，有效利用网络结构。该方法通过构造一个能够区分节点特征差异的估计器，实现对艺人人气的准确估计。本文给出了使用多个真实世界数据集的实验结果，这些数据集包含了最大的MSS之一Spotify中各种类型的艺术家。定量和定性评价表明，该方法对人气分类和回归都是有效的。
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, i.e., 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.