Carousel Personalization in Music Streaming Apps with Contextual Bandits
This addresses the challenge of selecting relevant items for users in media services, though it appears incremental by applying known bandit methods to a specific domain.
The paper tackled the problem of personalizing carousel recommendations in music streaming apps by modeling it as a contextual multi-armed bandit with multiple plays and cascade-based updates, and demonstrated its effectiveness on a large-scale playlist recommendation task.
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.