论文标题
Mostra：一个灵活的平衡框架，用于权衡的用户，艺术家和平台目标，用于音乐测序
Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing
论文作者
论文摘要
我们考虑了在音乐流平台上测序曲目的任务，目标是最大程度地提高用户满意度，而且是以艺术家和平台为中心的目标，以确保平台的长期健康和可持续性所需的目标。将工作跨越四个目标：SAT，发现，曝光和提升，我们强调了这些目标跨这些目标的需求和潜力，并提出了Mostra，这是一种基于Set Transformer的Endoder-decoder-decoder架构，配备了supperular多目标光束搜索解码。拟议的模型为系统设计师提供了平衡多个目标的能力，并动态控制对一个目标满足其他目标的影响。通过大规模音乐流平台的数据进行广泛的实验，我们对跨不同目标存在的权衡提出了见解，并证明了所提出的框架会导致在各种感兴趣的各种指标中达到了卓越的，即时的平衡。
We consider the task of sequencing tracks on music streaming platforms where the goal is to maximise not only user satisfaction, but also artist- and platform-centric objectives, needed to ensure long-term health and sustainability of the platform. Grounding the work across four objectives: Sat, Discovery, Exposure and Boost, we highlight the need and the potential to trade-off performance across these objectives, and propose Mostra, a Set Transformer-based encoder-decoder architecture equipped with submodular multi-objective beam search decoding. The proposed model affords system designers the power to balance multiple goals, and dynamically control the impact on one objective to satisfy other objectives. Through extensive experiments on data from a large-scale music streaming platform, we present insights on the trade-offs that exist across different objectives, and demonstrate that the proposed framework leads to a superior, just-in-time balancing across the various metrics of interest.
