Learn how to use BigQuery ML to train and deploy a recommendation system.
The majority of consumers today expect personalization, but how do you create a recommendation system and use the predicted recommendations for marketing activations, such as personalized emails or ad retargeting campaigns?
This is a step-by-step video that explores the e-commerce recommendation system referenced at goo.gle/3e4f1fU and as well as in this notebook (goo.gle/31O4JLM) environment and helps walk you through the entire process of building such a system in your organization.
You will learn how to:
Process sample data into a format suitable for training a matrix factorization model
Create, train, and deploy a matrix factorization model.
Get predictions from the deployed model about what products your customers are most likely to be interested in.
Export prediction data from BigQuery to Google Analytics 360, Cloud Storage, or programmatically reading it from the BigQuery table.
Solutions guide → goo.gle/2HDqoPJ
Notebook here → goo.gle/31O4JLM
More Smart Analytics Reference Patterns → goo.gle/2JcLGEJ
11 окт 2024
