Learning node embeddings in transaction networks /

"Presented by Jesse Barbour, Chief Data Scientist at Q2ebanking. Due to the specialized and sophisticated nature of many commercially focused financial products offered by banks and fintechs, building recommender systems around those products is especially difficult. Taking inspiration from the...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico Video
Idioma:Inglés
Publicado: [Austin, Texas] : Data Science Salon, 2020.
Temas:
Recommender systems (Information filtering)
Computer networks > Security measures.
Transaction systems (Computer systems)
Neural networks (Computer science)
Application software > Development.
Neural Networks, Computer
Systèmes de recommandation (Filtrage d'information)
Réseaux d'ordinateurs > Sécurité > Mesures.
Systèmes transactionnels.
Réseaux neuronaux (Informatique)
Logiciels d'application > Développement.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
Sumario:"Presented by Jesse Barbour, Chief Data Scientist at Q2ebanking. Due to the specialized and sophisticated nature of many commercially focused financial products offered by banks and fintechs, building recommender systems around those products is especially difficult. Taking inspiration from the field of neural language modeling, we will discuss an application of learning node embeddings on a large-scale financial transaction graph in order to solve this problem."--Resource description page
Notas:Title from resource description page (Safari, viewed November 3, 2020).
Place of publication from title screen.
Descripción Física:1 online resource (1 streaming video file (21 min., 22 sec.))

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