#Devdocs magento install
Select your preferred editor (can use Nano, Notepad++, or VIM)Īlthough you can install Git using Chocolatey, we chose to install Git for Windows independently for more control of the installation settings.Select Checkout as-is, commit Unix-style line endings.Open the Git Setup file downloaded from the Git for Windows site and use the following settings during installation wizard: Use Git for Windows to prevent interference with the existing Windows environment and to have Git Bash and Git GUI launch commands available on the shortcut menu. Important: If you encounter problems with Ruby, or the gem command is not recognized, you can install the **rubyinstaller-devkit.exe **development kit located in the c:\ProgramData\chocolatey\bin folder. Verify the environment variables were added properly: Open the Command Prompt using Run as Administrator in the shortcut menu. If you have Ruby installed on the workstation, then you can skip this installation. You can install editors, such as Nano and Notepad++, using Chocolatey, as well. As a best practice, only use extensions labeled as a "trusted package". Chocolately has many extensions available, similar to Homebrew for macOS.
You should see C:\ProgramData\chocolatey\bin in the path.Ĭlose and reopen the command prompt before using choco commands.Īfter running the script at the command line, you can install any required extensions.In the Windows CMD console, type echo %path%.In the Windows UI, open search and type path.Verify Chocolatey was added to the environment variables: The Product Recommendations service then deploys those recommendations to your storefront.-NoProfile -InputFormat None -ExecutionPolicy Bypass -Command "iex ((New-Object ).DownloadString(''))" & SET "PATH=%PATH% %ALLUSERSPROFILE%\chocolatey\bin" When you install the magento/product-recommendations module, Adobe Sensei aggregates the behavioral and catalog data, creating Product Recommendations for each recommendation type. Commerce and Adobe Sensei do not collect personally identifiable information.Ĭatalog - Product metadata, such as name, price, availability, and so on. Product Recommendations require the following data:īehavioral - Data from a shopper’s engagement on your site, such as product views, items added to a cart, and purchases. At this point, the merchant can create, manage, and deploy product recommendation units to their storefront directly from the Admin UI. Adobe Sensei processes this behavioral data along with your catalog data and calculates product associations that are leveraged by the recommendations service. Once the recommendation modules are installed and configured, your storefront will begin collecting behavioral data. Adobe Sensei intelligence services are leveraged on the SaaS side. The Commerce side includes the storefront, which contains the event collector and recommendations layout template, and the backend, which includes the Data Services, SaaS Export module, and the Admin UI. Architectural overviewĪt a high level, Commerce Product Recommendations are deployed as SaaS.
#Devdocs magento how to
If you use a custom frontend technology such as React or Vue JS, refer to the user guide to learn how to integrate Product Recommendations in a headless environment. If your storefront is implemented using PWA Studio, refer to the PWA documentation. This data, when combined with your Commerce catalog, results in highly engaging, relevant, and personalized experiences for the shopper.
Adobe Commerce Product Recommendations are powered by Adobe Sensei, which uses artificial intelligence and machine-learning algorithms to perform a deep analysis of aggregated shopper data. Product Recommendations are surfaced on the storefront in the form of units such as “Customers who viewed this product also viewed”, “Customers who bought this product also bought”, “Recommended for you”, and so on. Product Recommendations are a powerful marketing tool you can use to increase conversions, boost revenue, and stimulate shopper engagement.
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