You can combine several RS using the Advanced template in Bloomreach Engagement. This option allows you to create a customized model by combining more models from and setting additional options.

**The following engines are available in Bloomreach Engagement for you to combine**

NameWhich model it is
Content based[**Similar items template**](🔗)
Preferences of many users[**Personalized recommendations for you template**](🔗) (includes an arbitrary number of events with custom weights (weight is a positive number, where the higher the number, the higher the "weight" or "importance" of the event) to set their strength for indicating user similarity)
Manual selection[**Manual selection technique**](🔗)
New in stock[**New items template**](🔗)
Previously seen items[**Customer recent interactions template**](🔗)
Chosen by metric[**Chosen by metric template**](🔗)

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** + **Personalized 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:


Example of Advanced template with two models.

After you pick preferred engines, you can also choose a combination strategy for building the recommendations:

Combination nameWhat does it mean
Only the best oneWe will pick the best model according to its performance. Performance is measured after the live traffic reaches at least 1000 views (`recommendations.action` = `view`) of the results of all the engines. The best model is the one with the highest number of clicks (`recommendations.action` = `click`).
Combine multipleRecommendations will be mixed from all selected engines results.
Order by priority of models _(you can pick the priorities in the previous step while designing the engines)_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 recommendation model is not able to personalize.

Combination strategies explained visually.

Combining models

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.