In Uber's ride-hailing business, a driver picks up a user from a curbside or other location, and then drops them off at their destination, completing a trip. Uber Eats, our food delivery service, faces a more complex trip model. When a user requests a food order in the app, the specified restaurant begins preparing the order. When that order is ready, we dispatch a delivery-partner to pick it up and bring it to the eater. Modeling the real world logistics that go into an Uber Eats trip is a complex problem.

For eaters, our system offers personalized restaurant recommendations, but ultimately eaters are looking for specific dishes to order. So, we are working on taking our recommendations to the dish level, creating more tailored eater experiences. This is analogous to the music industry's shift from selling albums to selling songs, and we believe it will be a huge leap forward in terms of the experience we can provide. In addition, for new eaters that are checking out the platform, we are working on methods to bootstrap our recommendations and solve the cold start problem often seen in recommender systems. For restaurant-partners, we are working to balance the surfacing of promotions and deals offered to eaters, as these short-term initiatives create interesting effects on the system by changing the behaviors of eaters who respond to them.