The Evolution of DoorDash’s Recommendations Algorithm for Grocery Substitutions
Building a recommendations model from scratch is a challenge that many start-ups face. When DoorDash first entered the grocery & convenience space in 2020, it was critical to recommend good substitutes when certain items that customers ordered were out-of-stock at the store. In this talk, I’ll provide an overview of how we tackled the cold-start problem and how we evolved the recommendations model over time as more data was collected. I’ll dive into three distinct phases of the model evolution, in which the final phase uses a DLRM model. Along the way, we’ll uncover some interesting consumer patterns, such as whether Pepsi and Coca-Cola are substitutable.
Dawn Lu is a senior data scientist on the Machine Learning team at DoorDash, where she has built several foundational ML systems over the past 4 years. As the first data scientist to work on DoorDash’s new verticals team, Dawn led the development of fulfillment initiatives for the grocery & convenience verticals. Prior to that, she focused on building predictive models to power DoorDash’s logistics engine, such as architecting a new driver pay model and improving ETA accuracy during hyper-growth. She holds a bachelor’s degree in Economics from Yale University.