Skip to content

πŸ€– Build a serverless AI-powered product recommendation engine using AWS Lambda and DynamoDB for personalized shopping experiences.

License

Notifications You must be signed in to change notification settings

Do91-NL/AI-Powered-Product-Recommendation-Engine-using-AWS

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Β 

History

29 Commits
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 

Repository files navigation

πŸ€– AI-Powered-Product-Recommendation-Engine-using-AWS - Get Real-Time Product Suggestions Easily

Download Now

πŸš€ Getting Started

The AI-Powered Product Recommendation Engine helps you find products you will love. It uses artificial intelligence to suggest items based on your preferences. Follow these steps to download and run the application.

πŸ“¦ Features

  • Personalized product suggestions tailored to your interests.
  • Built on a secure and scalable architecture using AWS services.
  • Real-time recommendations with low latency.
  • User-friendly interface that simplifies navigation.

πŸ“₯ Download & Install

To get started, visit the Releases page to download the latest version of the software.

πŸ‘‰ Visit the Releases Page to Download

🌐 System Requirements

  • Windows, macOS, or Linux operating system.
  • Internet connection for real-time recommendations.
  • Minimum RAM: 4 GB.
  • Recommended storage space: 500 MB.

πŸ›  Installation Steps

  1. Visit the Releases Page: Click the link below to navigate to the downloads page.

    πŸ‘‰ Visit the Releases Page to Download

  2. Select the Latest Version: Identify the most recent release listed on the page.

  3. Download the File: Click on the download link corresponding to your operating system.

  4. Open the File:

    • Windows: Double-click the downloaded .exe file to start the installation.
    • macOS: Open the downloaded .dmg file and drag the application to your Applications folder.
    • Linux: Depending on your distribution, open a terminal and execute the package manager command for installation.
  5. Follow Installation Prompts: Follow the on-screen instructions to complete the installation.

  6. Run the Application: Locate the application on your computer and open it to start receiving product recommendations.

πŸ–₯️ How to Use

  1. Create an Account: Register with your email to store your preferences.
  2. Provide Preferences: Fill out a simple questionnaire about your interests.
  3. Explore Recommendations: Enjoy personalized product suggestions tailored just for you.

πŸ“š Topics Covered

  • AI: Leveraging cutting-edge technology for smart recommendations.
  • Amazon Personalize: A robust tool used for creating personalized experiences.
  • API Gateway: Handling requests seamlessly for smooth interactions.
  • AWS Services: Utilizing Amazon Web Services for scalability and reliability.
  • DynamoDB: Ensuring quick database access and high performance.
  • CloudFront: Delivering fast and secure content.
  • React: Creating a responsive and easy-to-use interface.
  • Serverless Architecture: Reducing overhead and increasing efficiency.

🏁 Troubleshooting

If you encounter issues while using the application, try these steps:

  • Check Internet Connection: Ensure that you have a stable internet connection.
  • Clear Cache: Sometimes, clearing your application cache helps resolve issues.
  • Restart the Application: Close and reopen the application if it becomes unresponsive.
  • Consult Documentation: Visit the repository for troubleshooting tips and additional support.

πŸ“ž Support

For further assistance, you can reach out through the following:

  • Open an issue on the repository's GitHub page.
  • Join the community forum for real-time discussions and help.

Thank you for choosing our AI-Powered Product Recommendation Engine. Enjoy discovering your next favorite product!

About

πŸ€– Build a serverless AI-powered product recommendation engine using AWS Lambda and DynamoDB for personalized shopping experiences.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 2

  •  
  •