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Parallel and Distributed Systems Report Series Understanding User Behavior in Spotify

Spotify is a peer-assisted music streaming service that offers instant access to a vast music catalogue, and it has gained worldwide popularity in the past few years. Until now, little has been published about user behavior in such services. In this paper, we study the user behavior in Spotify by analyzing a massive dataset collected between 2010 and 2011. Firstly, we investigate the system dynamics including session arrival patterns, daily variations of session length, and playback arrival patterns. Secondly, we analyze the behavioral patterns of individual users on multiple devices and single devices, respectively. Our analysis reveals the patterns of users switching between multiple devices on successive sessions and the favorite times of day for Spotify users. We also show the correlations between both the length and the downtime of successive user sessions on single devices. Our findings not only deepen our understanding of user behavior in Spotify, but also shed light on user behavior in other music streaming services. We believe the results may also be used for building other types of services such as P2P social networks. In particular, our analysis of mobile user behavior provides valuable insights for developing systems on mobile platforms.