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

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, 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.

Network Analyses for Cross-Cultural Music Popularity

“…Networks have long been used to analyze and visualize relationship structures in music. In predicting artist popularity, Matsumoto et al [23] constructed a context-aware network combining Spotify-based audio features with biographic metadata and 'related artist' lists. South et al [24] examined a dataset of musical collaborations on Spotify, and used eigencentrality to estimate the influence of artists on that dataset.…”