Learning node embeddings in transaction networks / Data Science Salon.

Format:

Video

Contributor:

Barbour, Jesse, on-screen presenter.

Data Science Salon, publisher.

Language:

English

Subjects (All):

Recommender systems (Information filtering).

Computer networks--Security measures.

Computer networks.

Transaction systems (Computer systems).

Neural networks (Computer science).

Application software--Development.

Application software.

Physical Description:

1 online resource (1 streaming video file (21 min., 22 sec.)) : digital, sound, color

Other Title:

Title on title screen: Building a recommender system from node embeddings on a transaction graph

Place of Publication:

[Austin, Texas] : Data Science Salon, 2020.

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.

Participant:

Presenter, Jesse Barbour.

Notes:

Title from resource description page (Safari, viewed November 3, 2020).

Place of publication from title screen.

OCLC:

1203113590