Brief Overview
Amazon Personalize enables businesses to deliver relevant, real-time recommendations without ML expertise. It supports diverse use cases from product suggestions to content discovery, driving revenue growth and customer satisfaction across e-commerce, media, travel, finance, education, and gaming industries.
Key Industries & Applications
E-Commerce
- Product recommendations
- Personalized homepage content
- Cart add-on suggestions
- Search result re-ranking
Media & Entertainment
- Video/movie recommendations
- Personalized music playlists
- Content discovery
- "Continue watching" suggestions
News & Publishing
- Personalized article feeds
- Related story suggestions
- Custom newsletter content
Travel & Hospitality
- Destination recommendations
- Hotel and flight suggestions
- Personalized activity packages
Retail & Fashion
- Style and outfit recommendations
- "Complete the look" suggestions
- Reorder reminders
Financial Services
- Product recommendations (cards, loans)
- Personalized investment suggestions
- Targeted offers
Education
- Course recommendations
- Personalized learning paths
- Skill-based content matching
Gaming
- Game recommendations
- In-game item suggestions
- Player matching
Available Recipes
| Recipe | Purpose |
|---|---|
| USER_PERSONALIZATION | Personalization per user. Items by purchases, views. Recommended for you. Popularity count. Most popular. |
| USER_SEGMENTATION | Item and attribute affinity. |
| PERSONALIZED_ACTIONS | Best action. |
| PERSONALIZED_RANKING | Re-rank search results for user. |
| RELATED_ITEMS | Customers who viewed x also-viewed. Frequently bought together. Similar items. |
| TRENDING_NOW | Currently trending content. |
Common Event Types
| Industry | Events |
|---|---|
| E-commerce | view, click, add_to_cart, purchase |
| Streaming | play, pause, complete, like |
| News | read, share, bookmark |
| Travel | search, book, favorite |
Business Benefits
- 10-30% increase in conversions
- Higher engagement and click-through rates
- Improved retention and reduced churn
- Automated personalization at scale
Hands-On Practice
This guide will walk you through building a recommendation system by AWS CDK in Python with 02 stacks:
- Build the infra such as dataset created in S3, configuring Dataset Group, Schema and Recipes (Part I).
- Create the Pipeline stack to orchestra the automatic workflow (Part II).
- Upload dataset, execute the State machine to create the solution (model training) and campaign (Part III).