Enhance recommendations in Uber Eats with graph convolutional networks /

"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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico Video
Idioma:Inglés
Publicado: [Place of publication not identified] : O'Reilly, [2020]
Temas:
Uber (Firm)
O'Reilly TensorFlow World > (2019 : > Santa Clara, Calif.)
TensorFlow.
Customer services > Management > Data processing.
Information visualization.
Machine learning.
Information visualization
Machine learning
Electronic videos.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
Sumario:"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
Notas:Title from title screen (viewed July 27, 2020).
Date of publication from resource description page.
Descripción Física:1 online resource (1 streaming video file (39 min., 27 sec.))

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