Ankit Jain is a senior research scientist at Uber AI, 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 food delivery, fraud detection to self-driving cars. Previously, he worked in variety of machine learning roles at Facebook, Bank Of America and other startups. He co-authored a book on machine learning titled TensorFlow Machine Learning Projects. Additionally, he’s been a featured speaker in many of the top AI conferences and universities and has published papers in several top conferences like Neurips, ICLR. He earned his MS from UC Berkeley and BS from IIT Bombay (India).
Day 1 - Tech Talks 28 May
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.