Cold-Starting Podcast Ads and Promotions with Multi-Task Learning on Spotify

TL;DR

This paper introduces a multi-task learning model for podcast ad and promotion targeting on Spotify, improving cold-start performance and user engagement through transfer learning and joint optimization.

Contribution

It presents a unified multi-objective model that enhances personalization and cold-start capabilities for new advertising and promotional objectives in a large-scale podcast ecosystem.

Findings

Up to 22% reduction in effective Cost-Per-Stream (eCPS)

18-24% increase in podcast stream rates

Improved cold-start performance and system maintainability

Abstract

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.