MAPS: Netflix’s Multimodal Asset Personalization at Scale

This post details the integration of CLIP embeddings into Netflix's artwork personalization system to address the cold-start problem for new titles. It explains how concatenating CLIP image embeddings with learned ID embeddings allows for immediate personalization based on visual themes, talent, and color palettes, enabling knowledge transfer across titles and canvases. The post also describes model consolidation from five per-canvas models to one unified model due to CLIP's crop invariance. It introduces reward-based weighting for mixing training data across disparate canvases, optimizing for long-term member satisfaction, and discusses the use of inverse propensity scoring on exploration traffic for offline evaluation to mitigate bias from the production policy.