From Clicks to Conversions: Architecting Shopping Conversion Candidate Generation at Pinterest

This post details the architectural evolution of Pinterest's shopping conversion candidate generation model. Key contributions include the design of a parallel DCN v2 and MLP cross layers architecture, which improved offline recall by +11% and was adopted across production engagement retrieval models. It also describes the transition from a multi-head to a unified single-head multi-task architecture, incorporating an advertiser-level loss function to better stabilize query embeddings in low conversion coverage regions. This led to an average increase of +42% recall@100 for conversion tasks compared to the previous model.