You can combine several RS using the Advanced template in Bloomreach Engagement. This option allows you to create a customized model when you combine more models and set additional options.
**The following engines are available in Bloomreach Engagement for you to combine**
|Name||Which model is it|
|Manual selection||[**Manual selection technique**](🔗)|
|New items||[**New items template**](🔗)|
|Chosen by metric||[**Chosen by metric template**](🔗)|
|More like this||[**More like this template**](🔗)|
|Textual similarity||[**Text model**](🔗)|
|Customer recent interactions||[**Customer recent interactions template**](🔗)|
|Personalized recommendations for you||[**Personalized recommendations for you template**](🔗) This model lets you add as many events as you want, each with its own custom weight. The weight is a positive number that shows the event's importance. The bigger the number, the stronger the event signals user similarity.|
# Setup Guide
### 1. Choose a Catalog
Choose a product catalog that contains all your products and set a catalog filter to filter our only relevant products. Typically, it's used to filter only available products.
### 2. Choose and Set Up the Models
You can add any model from the list by navigating to `
Choose and set up the recommendation models` and hitting the “+ Add engine”:
This example combines two models: **More like this** and **Personalized recommendations for you**, with priorities 1 and 2 respectively.
You will first get all results from the **More like this** model (since it's priority 1) and then results from the **Personalized recommendations for you model** will follow (as it's priority 2).
### 3. Select Combination Strategy
After you pick preferred engines, you can also choose a combination strategy for building the recommendations:
|Combination name||What does it mean|
|Only the best one||We will pick the best model according to its performance. Performance is measured after the live traffic reaches at least 1000 views (`|
|Combine multiple||Recommendations 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 are made using engines in order of their priority (1 is the highest). This means we'll first use the engine with priority 1. If this engine doesn't provide enough results, we'll use the engine with priority 2, and so on, until we've returned the needed number of items. This method helps define custom backups in case a recommendation model can't personalize.|
It's recommended to mix different engines in a model, especially for email recommendations, so customers get the most relevant suggestions. As the lowest priority (the biggest number), we suggest using a manual selection engine as your backup if the previous engines don't give any or enough results.
Keep in mind that the more models you combine, the longer the API response time becomes.