Enhance Recommendations in Uber Eats with Graph Convolutional Networks
Uber Eats has become synonymous to online food ordering. With increasing selection of restaurants and dishes in the app, personalization is quite crucial to drive growth. One aspect of personalization is better recommendation of restaurants and dishes to the users so they can get the right food at the right time.
In this talk, we present how to augment the ranking models with better representations of users, dishes and restaurants. Specifically, we show how to leverage the graph structure of Uber Eats data to learn node embeddings of various entities using state of the art Graph Convolutional Networks implemented in Tensorflow. We also show that these methods perform better than standard Matrix Factorization approaches for our use case.
Key Takeaway - The audience will learn about how to build deep learning models on graph data using Graph Convolutional Networks to obtain better entity representations to use for recommendation. They will also learn about strategies to scale the model to very big datasets.
Biography: Piero Molino is a Senior Research Scientist at Uber AI with focus on machine learning for language and dialogue. Piero completed a PhD on Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence, where he worked on grounded language understanding. After Uber acquired Geometric Intelligence, he became one of the founding members of Uber AI Labs. He currently leads the development of Ludwig, a code-free deep learning framework.