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.

本文针对音乐平台等媒体服务提供商经常使用轮播卡片同用户推荐个性化内容的挑战，将轮播卡片的个性化视为多臂赌博问题，基于级联更新和延迟批量反馈提出了一个框架，并通过实验验证了该框架在全球音乐平台上的可用性。此外，本文公开了实验的工业数据，以及一个开源环境，用于模拟类似的轮播卡片个性化学习问题。