Enhance recommendations in Uber Eats with graph convolutional networks / Ankit Jain, Piero Molino.

Author
Jain, Ankit [Browse]
Format
Video/Projected medium
Language
English
Published/​Created
[Place of publication not identified] : O'Reilly, [2020]
Description
1 online resource.

Details

Subject(s)
On-screen presenter
Library of Congress genre(s)
Series
Safari Books Online (Series) [More in this series]
Summary note
"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." Recorded at the O'Reilly TensorFlow World conference, October 28-31, 2019, Santa Clara, CA.--Resource description page.
Notes
  • Date of publication from resource description page.
  • Title from title screen (viewed July 27, 2020).
Participant(s)/​Performer(s)
Presenters, Ankit Jain, Piero Molino.
OCLC
1179144091
Other standard number
  • 0636920373575
Statement on language in description
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