You can combine several RS using the Advanced template in Exponea. This option allows you to create a customized model by combining more models from and setting additional options.
The following engines are available in Exponea for you to combine
Which model it is
Preferences of many users
Personalised recommendations for you template
New in stock
Previously seen items
Chosen by metric
You can add any model from the list by navigating to “Choose and set up the recommendation models” and hitting the “+ Add engine”:
Example of combining two models (Similar items + Personalised recommendations for you) with priorities set as 1 and 2 (described below). In this example, all of the results from model Similar items will be returned at first (priority 1) and then all of the results from the model Personalized recommendations for you (priority 2) will follow:
After you pick preferred engines, you can also choose a combination strategy for building the recommendations:
What does it mean
Only the best one
We will pick the best model according to the performance. Performance is measured after the live traffic reaches at least 1000 views (
Recommendations will be mixed from all selected engines results.
Order by priority of models
Recommendations will be composed using engines ordered by their priority (1 is the highest one). Practically it means that we will primarily use the engine with priority 1 and if this engine will not be able to deliver enough results, we will use the engine with priority 2, and so on after the required number of items can be returned. It is useful to define custom fallbacks if any recommendations model is not able to personalize.
It is recommended, especially for email recommendations, to combine different engines in a model, so that customers get the most relevant recommendations. As the last priority (the highest number), we recommend having a manual selection engine that the model will default to if all the previous ones would fail to deliver any or enough results. Please note that more recommendation models you combine, the API latency is proportionally increasing.
Updated about a year ago
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