Ads Personalization with Sequential Modeling and Hetero-MMoE

Uber has enhanced its ads personalization system by incorporating sequential user behavior modeling with a target-aware transformer encoder and a novel Hetero-MMoE architecture. This evolution moves beyond static, aggregate features to capture temporal dynamics and evolving user intent using multi-head latent attention for efficient sequence summarization. The Hetero-MMoE framework blends MLP, DCN, and CIN experts to capture low- to high-order feature interactions more effectively, enabling more precise, context-aware ad predictions across diverse objectives. The system achieved significant performance gains in online experiments, leading to measurable improvements in revenue impact, user engagement, and advertiser outcomes.