| Description |
1 online resource (1 streaming video file (21 min., 22 sec.)) |
| Performer |
Presenter, Jesse Barbour. |
| Note |
Title from resource description page (Safari, viewed November 3, 2020). |
|
Place of publication from title screen. |
| Summary |
"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 |
| Subject |
Recommender systems (Information filtering)
|
|
Computer networks -- Security measures.
|
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Transaction systems (Computer systems)
|
|
Neural networks (Computer science)
|
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Application software -- Development.
|
|
Neural Networks, Computer |
|
Systèmes de recommandation (Filtrage d'information) |
|
Réseaux d'ordinateurs -- Sécurité -- Mesures. |
|
Systèmes transactionnels. |
|
Réseaux neuronaux (Informatique) |
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Logiciels d'application -- Développement. |
|
Application software -- Development. |
|
Computer networks -- Security measures. |
|
Neural networks (Computer science) |
|
Recommender systems (Information filtering) |
|
Transaction systems (Computer systems) |
| Added Author |
Barbour, Jesse, on-screen presenter.
|
|
Data Science Salon, publisher.
|
| Note |
Title on title screen: Building a recommender system from node embeddings on a transaction graph |
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