Personalization At Doordash: From Conversion Modeling To Multi-objective Long-term Value Optimization

Doordash is one of the largest platform in the world to connect millions of local business with customers. We use advanced machine learning technologies to build a personalized customer experience and help customers discover a variant of local businesses they love. In this talk, we will introduce a few technologies we used to build our personalized homepage experience and the lessons learned during the process. Customers use our platform in different ways, they can browse on homepage, search on search bar or respond to a push notification or an email sent to them. There are also different types of actions they can take during their shopping journeys, included but not limited to views, (good) clicks, add-to-cart, and checkout. We will first introduce how we leverage customers various action sequence and transformer to build our user interest model to understand customer interests. Doordash homepage has a very vivid design containing different components and complex layout to serve our customers. The stores are organized with themes into an UI component that we call carousel. The stores, carousels and other UI components are mixed on our homepage to showcase a diverse set of options and deals customers can choose from. The complex homepage design poses challenges for homepage ranking. We build a heterogeneous ranking system to rank different type of components in a 2-D layout. Traditionally, our ranking model is optimized for conversion. However, as our business grows, we have multiple business objectives to care about. In the meanwhile, we also want to optimize for customers long term satisfaction so we can sustain and grow our platform. We will describe how do we model customers long term value and build a multi-objective ranking and optimization system to optimize and balance multiple business objectives.