Recommender Systems
Pinterest's recommendation systems are built on three core principles: leveraging lifelong user activity sequences to understand evolving preferences, deploying unified models that serve multiple tasks and surfaces for consistency, and maintaining infrastructure budget-aware costs to ensure sustainable scale across hundreds of millions of users.

Our most recent advances include lifelong user sequence modeling that captures nuanced behavioral patterns over extended periods, foundation ranking models that generalize across diverse recommendation scenarios, and generative recommender systems that create personalized content experiences.