Spotify separates personalization and experimentation into distinct tech stacks rather than combining them. Personalization systems (recommendation engines, contextual bandits, ML models) live in the ML platform for low-latency inference and rich feature access, while experimentation tools evaluate these systems through A/B tests. This separation avoids technical debt, enables better infrastructure optimization for each domain, and allows hundreds of teams to run thousands of experiments simultaneously. Multi-armed bandits are avoided for experimentation due to single-metric optimization limitations and complexity with multiple objectives.
Table of contents
IntroductionWhat is personalization?The overlap between personalization and experimentationWhy we separate experimentation and personalization at SpotifyThe one-dimensional focus of multi-armed banditsHow we run personalization experiments efficientlyWrapping upSort: