The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure
This addresses the problem of understanding user engagement for music streaming services, but it is incremental as it builds on existing work in behavioral analysis and music structure prediction.
The study investigated user skipping behavior in music streaming services and found a correlation between skip timing and musical structure, showing this pattern is consistent across users and time. Using user data to train a predictor improved accuracy significantly over traditional hand-labeled methods.
The behavior of users of music streaming services is investigated from the point of view of the temporal dimension of individual songs; specifically, the main object of the analysis is the point in time within a song at which users stop listening and start streaming another song ("skip"). The main contribution of this study is the ascertainment of a correlation between the distribution in time of skipping events and the musical structure of songs. It is also shown that such distribution is not only specific to the individual songs, but also independent of the cohort of users and, under stationary conditions, date of observation. Finally, user behavioral data is used to train a predictor of the musical structure of a song solely from its acoustic content; it is shown that the use of such data, available in large quantities to music streaming services, yields significant improvements in accuracy over the customary fashion of training this class of algorithms, in which only smaller amounts of hand-labeled data are available.