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Configure a Rule-Based Recommender
Leverage business logic and Data 360 calculated insights to mathematically determine a list of personalized recommendations, enhancing user experience and driving engagement.
Required Editions
| User Permissions Needed | |
|---|---|
| To configure recommenders: | Change and Edit Recommenders |
- From the App Launcher, search for and select Recommenders.
- Click New.
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In the Recommender Properties window, select a data space.
The data space that you choose determines which data graphs are available for you to select.
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Select a profile data graph.
The profile data graph that you choose determines which profiles are eligible to receive personalized content, and which profile-related attributes the personalization decision process can use. The menu lists only the profile data graphs in the selected data space.
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Select an item data graph to use for personalization response training.
The item data graph returns item-specific or catalog-specific content based on the data graph root object. The recommender uses this content when selecting what to return to the user in a personalized response. The menu lists only the item data graphs in the selected data space with Unified Individual, Individual, or Account as their primary DMO.
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Enter a recommender name and an optional description.
The Recommender API Name is autogenerated.
- (Optional) Activate a fallback recommender. You can activate a fallback recommender at any time. See Set Up a Fallback Recommender.
- Click Next.
- Select Rule-Based Recommendations, and then click Next.
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Select the data graph resource to provide recommendations.
You can select either an item data graph or a profile data graph resource.
- Item data graph provides direct attributes and calculated insights.
- Profile data graph provides calculated insights and engagement attributes related to recommendable items, such as products or brands.
Important If you select a profile data graph engagement attribute such as engagement date to recommend recently viewed items, create a profile data graph filter. Configure the filter to connect the item data graph product attribute with the related profile data graph's engagement attribute. This filter makes sure that Salesforce Personalization picks only the items that your customers recently viewed. -
(Optional) Add filters.
You can add filters to a recommender at any time. See Add Recommendation Filters.
- Save the recommender.
Salesforce Personalization creates the recommender. You can select the recommender for any personalization point decision.
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