February 2020
Intermediate to advanced
39m
English
conference
Enhance recommendations in Uber Eats with graph convolutional networks
by Ankit Jain, Piero Molino
Closed Captioning available in German, English, Spanish, French, Japanese, Korean, Portuguese (Portugal, Brazil), Chinese (Simplified), Chinese (Traditional)
Overview
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 (Uber AI Labs) 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.
Prerequisite knowledge
- 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
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