We present a unified multi-objective model for targeting both advertisements and promotions within the Spotify podcast ecosystem. Our approach addresses key challenges in personalization and cold-start initialization, particularly for new advertising objectives. By leveraging transfer learning from large-scale ad and content interactions within a multi-task learning (MTL) framework, a single joint model can be fine-tuned or directly applied to new or low-data targeting tasks, including in-app promotions. This multi-objective design jointly optimizes podcast outcomes such as streams, clicks, and follows for both ads and promotions using a shared representation over user, content, context, and creative features, effectively supporting diverse business goals while improving user experience. Online A/B tests show up to a 22% reduction in effective Cost-Per-Stream (eCPS), particularly for less-streamed podcasts, and an 18-24% increase in podcast stream rates. Offline experiments and ablations highlight the contribution of ancillary objectives and feature groups to cold-start performance. Our experience shows that a unified modeling strategy improves maintainability, cold-start performance, and coverage, while breaking down historically siloed targeting pipelines. We discuss practical trade-offs of such joint models in a real-world advertising system.

Budget management is an essential capability in many online applications, including running advertising campaigns in sponsored search and allocating promotional content in recommender systems (RS). Most existing approaches to optimising budget spendings rely on improving worst-case approximation guarantees in the respective online knapsack packing problem or leveraging online learning techniques to improve an average system performance. However, worst-case approaches often underperform in practice, as extreme scenarios are uncommon, while online learning methods may lack robustness in highly non-stationary environments. In our work, we bridge these two approaches by developing an online budget pacing algorithm that preserves worst-case guarantees while improving the average allocative efficiency of budget management systems.