Enhancing Ad Relevance: Integrating Real-Time Context into Sequential Recommender Models

Summary

Pinterest's sequential ads candidate generator inferred user embeddings offline from historical offsite conversions, which lacked real-time browsing context. Consequently, less than 1% of impressions on the Related Pins surface were attributed to this generator because its retrieved candidates struggled in downstream ranking. To resolve this limitation, engineers developed the Contextual Sequential Two Tower Model, integrating a context layer directly into the query tower alongside user demographic features. Offline training utilized synthetic context derived from positive conversion labels paired with high dropout rates to preserve sequence reliance, while serving split into offline Transformer inference and online context computation. Offline evaluations showed a 3x to 10x increase in Recall@K, and production deployment lifted median candidate relevance by 275% to 300% and overall ROAS by approximately 0.7%.

Takeaways

1. 1

   Offline evaluation of the contextual model demonstrated a 3x to 10x increase in Recall@K over the production baseline on Related Pins.

2. 2

   Median relevance of retrieved candidates increased by roughly 275% to 300%, doubling the volume of retrieved candidates delivered to impression.

3. 3

   Overall ads relevance metric rose by 1.08%, driving a ~0.7% lift in Return on Ad Spend (ROAS) overall and ~1.4% in top revenue countries.