SCALING CONDITIONAL LEARNED RETRIEVAL FOR PINTEREST HOME FEED

Summary from the TLDR Data newsletter

Pinterest scaled candidate generation by conditioning retrieval embeddings on explicit contexts such as interests, boards, and pins. The system combines conditioned sequence modeling, semantic IDs, unified models, slot routing, request deduplication, and GPU serving so one home feed can represent multiple user intents without exploding serving cost.