Why Choose This Project?

Personalized recommendations are a core part of modern applications like Netflix, Amazon, Spotify, and YouTube. This project leverages Amazon Personalize, a fully managed machine learning service by AWS that allows you to easily build, train, and deploy real-time personalized recommendation systems without requiring ML expertise.

It’s ideal for students or developers who want to explore ML-as-a-Service, recommendation engines, and real-time personalization.

Core Features

• Real-time personalized recommendations (products, videos, music, etc.)

• User-based and item-based filtering

• Related item suggestions ("Users who liked this also liked…")

• Ranking items based on user interest

• Seamless integration into websites or mobile apps

• Optional: Track user events for better personalization

Technology Stack

Architecture Workflow

1. Upload Dataset:

   • Upload user-item interaction data (CSV) to Amazon S3.

   • Typical format: user_id, item_id, timestamp, interaction_type.

2. Create Dataset Group & Schema:

   • Define schemas for interactions, users, and items.

   • Create a dataset group in Amazon Personalize.

3. Train Recommender Model:

   • Use built-in algorithms like User-Personalization, HRNN, SIMS.

4. Deploy Campaign:

   • Deploy the model to get real-time recommendations via API.

5. Integration with Frontend:

   • Use JavaScript or React to call the deployed campaign and display recommendations dynamically.

6. Optional:

   • Use Amazon Personalize Event Tracker to collect real-time user behavior for dynamic personalization.