omar16100 / amazon-personalize-samples

Notebooks and examples on how to onboard and use various features of Amazon Personalize

Amazon Personalize Samples

Notebooks and examples on how to onboard and use various features of Amazon Personalize

Getting Started with the Amazon Personalize

The getting_started/ folder contains a CloudFormation template that will deploy all the resources you need to build your first campaign with Amazon Personalize.

The notebooks provided can also serve as a template to building your own models with your own data. This repository is cloned into the environment so you can explore the more advanced notebooks with this approach as well.

Amazon Personalize Use Cases examples

The core_use_cases/ folder contains detailed examples of the following typical use cases.

• Collaborative filtering based on user-item interaction tables. The intuition behind is that similar users like similar items.
  • Offline evaluation with 'hrnn' user-based recommendation.
  • Example of 'sims' item-based recommendation.
  • How recommendation changes after 'put_events'.
• Hybrid recommendation also considering user, item, and event meta-data. The result is to extrapolate to out-of-sample users and items, based on their meta-data features.
  • How to use user, item, and event 'meta-data'.
  • Exploring 'cold-start' or 'future' items.

Scalable Operations examples for your Amazon Personalize deployments

The operations/ folder contains examples on the following topics:

• MLOps
  • This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Step Functions. To get started navigate to the ml_ops folder and follow the README instructions.
• Lambda Examples
  • This folder starts with a basic example of integrating put_events into your Personalize Campaigns by using Lambda functions processing new data from S3. To get started navigate to the lambda_examples folder and follow the README instructions.

Reference Architectures

The reference_architectures/ folder contains reference architectures for the following industries:

• Retail
• Media and Entertainment
• Travel and Hospitality

Workshops

The workshops/ folder contains a list of our most current workshops:

• POC in a Box
• Re:invent 2019

Data Science Tools

The data_science/ folder contains an example on how to approach visualization of the key properties of your input datasets.

The key components we look out for include:

• Missing data, duplicated events, and repeated item consumptions
• Power-law distribution of categorical fields
• Temporal drift analysis for cold-start applicability
• Analysis on user-session distribution

License Summary

This sample code is made available under a modified MIT license. See the LICENSE file.