Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations

To best understand how we made our Uber Eats recommendations more accurate, it helps to know the basics of how graph learning works. Many machine learning tasks can be performed on data structured as graphs by learning representations of the nodes. The representations that we learn from graphs can encode properties of the structure of the graph and be easily used for the above-mentioned machine learning tasks. For example, to represent an eater in our Uber Eats model we don't only use order history to inform order suggestions, but also information about what food items are connected to past Uber Eats orders and insights about similar users.