Build a recommendation system with Recommendations AI
This page provides step-by-step guidance for implementing a solution using Recommendations AI.
Before you begin
You can use Recommendations AI to get personalized recommendations for your website whether or not you are using Google tools. However, if you are using Google Tag Manager or Google Merchant Center, some steps to implement Recommendations AI are simplified.
Never cache personalized results from an end user, and never return personalized results to a different end user.
Implementing Recommendations AI with Google tools
This process is the fastest way to get to an A/B test using Recommendations AI and Google tools (Google Tag Manager and Google Merchant Center).
|1. Set up a Google Cloud project||You can use an existing Google Cloud project if you have one already.|
|2a. Import your product catalog using Merchant Center||You can also directly import your product catalog, but using Merchant Center reduces the steps needed to import your catalog.|
|2b. Configure Tag Manager to record user events||User events track user actions such as clicking on a product, adding an item to a shopping cart, or purchasing an item. You can start recording user events in parallel to the catalog import. Once the catalog import is complete, rejoin any events that were uploaded before the import completed.|
|3. Create your model||
After you have met the minimum data requirements, create your model to initiate model training. Initial model training and tuning takes 2-5 days to complete.
|4. Create your placements and preview your recommendations||
After your model has finished training and tuning, you create your placements and preview the recommendations from your model to ensure your setup is functioning as expected.
|5. Set up your A/B experiment||An A/B experiment gives you insight into how the Recommendations AI recommendations are affecting user behavior.|
Implementing Recommendations AI without additional Google tools
If you are not using Tag Manager and Merchant Center, use the following steps to integrate Recommendations AI into your website:
|1. Set up a Google Cloud project||
To use Recommendations AI, you must create a Google Cloud (GCP) project and create authentication credentials including an API key and an OAuth token (either using a user account or a service account) to access the project.
|2a. Import your product catalog||
You can add items to your Recommendations AI product catalog
individually by using the
|2b. Record user events||
User events track user actions such as clicking on a product, adding an item to a shopping cart, or purchasing an item, and so on. Recommendations AI relies on user event data in order to generate personalized recommendations. User events need to be ingested in real time to accurately reflect the behavior of your users.
You can start recording user events in parallel to the catalog import. Once the catalog import is complete, rejoin any events that were uploaded before the import completed.
|4. Determine your recommendation types and placements||
The location of the recommendation panel and the objective for that panel impact model tuning. Reviewing the available recommendation types, optimization objectives, and other model tuning options to determine the best options for your business objectives.
|5. Import historical user events||
Your models need sufficient training data before they can provide accurate predictions. Providing historical user event data enables you to start model training without having to wait months for enough user event data to be collected from your site. Learn more.
|6. Create your model||
After you have met the data requirements, create your model to initiate model training. Initial model training and tuning takes 2-5 days to complete.
|7. Create your placements and preview your recommendations||
After your model has been activated, you can create your placements and preview the recommendations from your model to ensure your setup is functioning as expected.
|8. Set up an A/B experiment (Optional)||
You can compare the performance of your website with Recommendations AI recommendations to a baseline version of your website without Recommendations AI recommendations.
|9. Evaluate your model||
You can associate recommendations and user events and Recommendations AI provides reporting of metrics to help you determine how incorporating the recommendations is affecting your business.
You can view recommendation metrics for your project in the Dashboard tab of the Recommendations AI Console.
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