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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.
| Objective-Based Recommendations | Rule-Based Recommendations |
|---|---|
| Uses deep learning algorithms on activity and behavioral data associated with a unique individual to provide personalized experiences. | Relies on calculated insights from the item data graph to rank and sort recommendable content. |
| Uses deep learning to predict which recommendations to present to an individual to best achieve the selected business goal. | Generates recommendations based on mathematical calculations associated with specific profile attributes (like sales or views) or item attributes (like price, brand, or publish date). |
| Eligible individuals receive their own, unique set of recommendations based on what the algorithm determines to accomplish the selected business objective. | Eligible individuals receive recommendations based on attributes included in the selected calculated insight. |
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