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.
Ankit currently works as a Senior Research Scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of Deep Learning methods to a variety of Uber’s problems ranging from forecasting, food delivery to self driving cars. Previously, he has worked in variety of data science roles at Bank of America, Facebook and other startups. He has co-authored a book on machine learning titled “Tensorflow Machine Learning Projects”. Additionally, he has been a featured speaker in many of the top AI conferences and universities across US including UC Berkeley, OReilly AI conference etc. He completed his MS from UC Berkeley and BS from IIT Bombay (India).