/Retail-Product-Predictions

This sample is part of the demo show in episode #2 for the Mechanics Series on Azure Cosmos DB for building intelligent apps. This sample demonstrates how to build a product recommendation system using the Alternating Least Squares algorithm with data from the .NET eShop sample.

Primary LanguageJupyter NotebookMIT LicenseMIT

Retail Product Predictions using ALS in pyspark

In this solution we will use Azure Cosmos DB for NoSQL (and Azure Cosmos DB for MongoDB) to demonstrate how you can build a purchasing prediction feature for an Ecommerce retail workload using collaborative filtering with the Alternative Least Squares model. Collaborative Filtering is the most implemented and mature recommendation system and the Alternative Least Squares (ALS) model is one of the most popular method in collaborative filtering.

Project

This repo has been populated by an initial template to help get you started. Please make sure to update the content to build a great experience for community-building.

As the maintainer of this project, please make a few updates:

  • Improving this README.MD file to provide a great experience
  • Updating SUPPORT.MD with content about this project's support experience
  • Understanding the security reporting process in SECURITY.MD
  • Remove this section from the README

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.