Enhance recommendations in Uber Eats with graph convolutional networks
Who is this presentation for?
- Data scientists, machine learning engineers, and research scientists
Uber Eats has become synonymous with online food ordering. With an increasing selection of restaurants and dishes in the app, personalization is quite crucial to drive growth. One aspect of personalization is a better recommendation of restaurants and dishes so users can get the right food at the right time.
Ankit Jain and Piero Molino detail how to augment the ranking models with better representations of users, dishes, and restaurants. Specifically, they 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 and how these methods perform better than standard matrix factorization approaches for this use case.
- General knowledge of deep learning with TensorFlow
What you'll learn
- Learn how to build deep learning models on graph data using graph convolutional networks to obtain better entity representations to use for recommendation
- Discover strategies to scale a model to very big datasets
Uber AI Labs
Ankit Jain is 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 and food delivery to self-driving cars. Previously, he worked in variety of data science roles at Bank of America, Facebook, and other startups. He coauthored 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 across the US, including UC Berkeley and the O’Reilly AI Conference, among others. He earned his MS from UC Berkeley and BS from IIT Bombay (India).
Uber AI Labs
Piero Molino is a cofounder and senior research scientist at Uber AI Labs, where he works on natural language understanding and conversational AI. He’s the author of the open source platform Ludwig, a code-free deep learning toolbox.
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