https://github.com/azurecosmosdb/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.
https://github.com/azurecosmosdb/retail-product-predictions

Last synced: about 5 hours ago
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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.

• Host: GitHub
• URL: https://github.com/azurecosmosdb/retail-product-predictions
• Owner: AzureCosmosDB
• License: mit
• Created: 2024-04-05T22:01:30.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-06-23T12:51:03.000Z (5 months ago)
• Last Synced: 2024-06-24T06:53:01.399Z (5 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 2.43 MB
• Stars: 5
• Watchers: 5
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Security: SECURITY.md
  • Support: SUPPORT.md

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README

        
# 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.

- Learn more about [Azure Cosmos DB for NoSQL vector search](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search)

- Learn more about [vCore-based Azure Cosmos DB for MongoDB vector search](https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/vector-search)

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