How to build and deploy a recommendation system with BigQuery ML

Опубликовано 28 октября 2020, 16:01

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