论文标题
与上下文匪徒的音乐流应用程序中的旋转木马个性化
Carousel Personalization in Music Streaming Apps with Contextual Bandits
论文作者
论文摘要
音乐流媒体平台之类的媒体服务提供商经常利用可滑动的旋转木马向用户推荐个性化内容。但是，选择最相关的项目（专辑，艺术家，播放列表...）以在这些轮播中显示是一项艰巨的任务，因为项目很多，并且用户具有不同的喜好。在本文中，我们将旋转木马个性化建模为上下文的多武器强盗问题，具有多个戏剧，基于级联的更新和延迟的批处理反馈。我们从经验上展示了框架在捕获现实世界旋转木马特征方面的有效性，该框架通过在全球音乐流移动应用程序上解决了大规模的播放列表推荐任务。与本文一起，我们从实验中公开释放工业数据，以及一个开源环境，以模拟可比的旋转木马个性化学习问题。
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.
