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We consider the task of sequencing tracks on music streaming platforms wherethe goal is to maximise not only user satisfaction, but also artist- andplatform-centric objectives, needed to ensure long-term health andsustainability of the platform. Grounding the work across four objectives: Sat,Discovery, Exposure and Boost, we highlight the need and the potential totrade-off performance across these objectives, and propose Mostra, a SetTransformer-based encoder-decoder architecture equipped with submodularmulti-objective beam search decoding. The proposed model affords systemdesigners the power to balance multiple goals, and dynamically control theimpact on one objective to satisfy other objectives. Through extensiveexperiments on data from a large-scale music streaming platform, we presentinsights on the trade-offs that exist across different objectives, anddemonstrate that the proposed framework leads to a superior, just-in-timebalancing across the various metrics of interest.

Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing