A Data-driven Approach to Identifying Music Listener Groups based on Users' Playrate Distributions of Listening Events

Many studies have sought to understand the behavior of music listeners to design an improved music listening experience. This is especially important in music recommendation systems in that listening behavior can directly relate to the purpose of the system. For example, a listener who likes to discover new music will be more satisfied with a list of suggestions that present different types of music, while others prefer to listen to their same old music and artists. Previous research has focused on performing user research to explicitly extract information about listening behavior but few studies have attempted a data-driven approach to suggest listener personas or groups. In this study, we applied two clustering methods to user playrate distribution data to see if meaningful user clusters appear, and performed analysis on the results by comparing the patterns of the result clusters with the major characteristics of listener groups derived from previous user researches. Our experiments show that two large clusters and two small clusters are formed, with each cluster representing an intuitive difference in terms of listening behavior.