Video description
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.
Table of contents
Product information
- Title: Learning Node Embeddings in Transaction Networks
- Author(s):
- Release date: March 2020
- Publisher(s): Data Science Salon
- ISBN: None
You might also like
video
New Frontiers in ML driven Customer Intelligence
Seemit Sheth – Head of Data Science at Capital One Micah Price – Principal Associate Data …
video
All Models are Wrong, but Some are Useful. Especially with the Right Data
Presented by Alex Schwarm – VP/Head of Data Science at Dun & Bradstreet For many teams, …
video
Re-Inventing Customer Engagement Using Machine Learning
Consumers today are less brand loyal and primarily driven by rewards, benefits and experiences. Constant changing …
video
Challenges in Machine Learning from Model Building to Deployment at Scale
Presented by Anupama Joshi Companies are moving towards AI/Machine learning very fast. Data scientist are building …