Beyond Two Towers: Launching the 3-Tower Engagement Co-Train Model (Part 2)

This post details the development and implementation of a 3-tower engagement co-train model for shopping conversion ads, building upon the existing Nexus serving stack. The model architecture introduces a cross tower for richer interaction modeling and an inter module for shared representation, co-trained with a fast two-tower prediction head. Key technical contributions include partitioning query and Pin embeddings for task-specific segments, a weighted multi-task loss function, and significant serving optimizations like moving Pin pre-processing into the Pin tower and exploring lower precision inference. The model achieved ~30% reduction in offline loss for three-tower predictions and maintained comparable quality to the production two-tower model for fast scoring.