Several types of recommendation can be achieved with this engine , this includes:
- AI Personalized - Using Transformer models to predict the best product for each person based on the history of interaction events.
- Most recently engaged - which can be tied to any interaction event, (as long as that data is being tracked in Braze in reference to the catalog) including use cases such as:
- Recently Clicked
- Recently Viewed
- Recently Liked
- Recently Engaged
- Recently Purchased
- Most Popular - Which includes most commonly engaged with - tied to any interaction event. This can include
- Most Viewed
- Most Purchased
