Creating and Training Salesforce Personalization Recommenders

Salesforce Personalization in Marketing Cloud Next generates multichannel, focused, and individualized recommendations based off of deep, digital, and offline behavioral and business context data. To create personalized recommendations, you configure a recommender and train it using Data 360 data graphs that you select.

• Choosing a Recommendation Type
  The recommendation type defines the method that the recommender uses to provide content. You can choose either objective-based recommendations or rule-based recommendations. Objective-based recommendations use a deep learning model to produce personalized, targeted recommendations for an individual. Rule-based recommendations use business logic to mathematically determine a list of recommendations based on Data 360 calculated insights. For both recommendation types, you can add filters to include or exclude items.
• How Recommendation Models Work
  Objective-Based Recommenders use the Deep Learning for Personalized Recommendations (DLPR) model to deliver personalized recommendations that align with your business goals. DLPR powers every recommender.
• Configure a Recommender
  A recommender presents personalized recommendations to visitors based on the recommendation type and criteria that you select.
• Recommendation Filters
  Use Recommendation Filters to control which items the recommender suggests to your customers.
• Simulate a Recommender
  After you create a recommender and train it, you can preview recommendations for specific users and for the items that the user is viewing at the time the recommendations are presented. By previewing recommendation results, you can verify the simulation before publishing the recommender.
• Co-Bought Recommender with Multiple Dimension Filters Configuration Example
  Use calculated insights and the power of filters to configure a recommender to generate a list of products that have frequently been purchased together.
• Training a Personalization Recommender
  After you create a personalization recommender, it uses Customer 360 data from the selected data graphs to begin training. You can track the training on the recommender record.
• Set Up a Fallback Recommender
  Select a fallback recommender to use when your recommender doesn’t return sufficient recommendation items. If restrictive filters or insufficient data prevent a recommender from returning sufficient items, Salesforce Personalization uses the fallback recommender to fill the remaining slots.