Cold-Starting Podcast Ads with Multi-Task Learning

Spotify Research presents a unified multi-task learning (MTL) framework that jointly optimizes podcast ads and promotions within a single model, replacing previously siloed systems. The model shares a common encoder for user, content, and context representations, with task-specific prediction heads for streams, clicks, likes, and follows. Two key techniques—directional loss masking and source-balanced sampling—prevent negative transfer between channels. Offline ablations show the 5-task MTL model achieves +4.5% Promotions AP and +50.2% Ads AP over the promotions-only baseline. Online A/B tests across 180+ markets showed ~18% higher impression-to-stream rates, ~20% lower cost-per-stream, and ~18% more total streams. Cold-start gains were even larger for emerging creators, with up to ~60% improvement in impression-to-stream rate for low-popularity podcasts. The unified approach also reduces engineering complexity and accelerates new product launches.