  Media services providers, such as music streaming platforms, frequently
leverage swipeable carousels to recommend personalized content to their users.
However, selecting the most relevant items (albums, artists, playlists...) to
display in these carousels is a challenging task, as items are numerous and as
users have different preferences. In this paper, we model carousel
personalization as a contextual multi-armed bandit problem with multiple plays,
cascade-based updates and delayed batch feedback. We empirically show the
effectiveness of our framework at capturing characteristics of real-world
carousels by addressing a large-scale playlist recommendation task on a global
music streaming mobile app. Along with this paper, we publicly release
industrial data from our experiments, as well as an open-source environment to
simulate comparable carousel personalization learning problems.
