This solution enables you to create product recommendations predictive models based on historical transaction data and information on the product catalog. The solution exposes mechanisms to train models and request recommendations from those models.
> **Note:** If you have already deployed this solution, click [here](https://start.cortanaintelligence.com/Deployments?type=recommendationswebapp) to view your deployment. ### Estimated Provisioning Time: 3 Minutes # Recommendations Solution This solution enables you to create product recommendations predictive models based on historical transaction data and information on the product catalog. At a high level, the solution exposes mechanisms to: 1. Train models using the SAR (Smart Adaptive Recommendations) algorithm. 2. Request a previously created model for recommendations. The following scenarios are supported by the SAR algorithm: **Item-to-Item Recommendations.** This is the "Customers who liked this product also liked these other products" scenario. Increase the discoverability of items in your catalog by showing relevant products to your customers. **Personalized Recommendations.** By providing a user id or the recent history of transactions for a given user, the SAR algorithm can return personalized recommendations for that user. ## High level architecture This solution creates the Azure resources necessary and connects them to generate a scalable architecture. More specifically, it creates an Azure Resource Group in your Azure subscription with the following components: 1. An Azure WebApp (and a respective Web Job) The Azure Web-Application exposes a RESTful interface that allows you to train recommendations models, and then query those models for product recommendations. The Azure Web-Application also delegates training jobs to an Azure WebJob. 2. An Azure Storage subscription that is used for storing models, model metadata as well as for WebApp to WebJob communication. ![Diagram](https://caqsres.blob.core.windows.net/recommendationswebapp/highlevelarch.png) Learn more about the Recommendations Solution [here](http://github.com/Microsoft/Product-Recommendations). ## Disclaimer ©2017 Microsoft Corporation. All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third party data was used to generate the Solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licenses in order to create similar datasets.