This article provides you with descriptions of all existing recommendation models that you can find in `Campaigns > Recommendations > Create new`.

At the bottom of the article, you can find an [overview](🔗) of the templates, pinpointing:

• The template type

• Whether the template is personalized

• Whether Data Mapping is needed

• Which events are needed

• Which catalog attributes are needed

• Which request parameters are needed

### Properties

Under each model, there is a list of the following properties:

Explanations to model types:

• **Rule-based:** Based on fixed rules. These models are ready to serve immediately after you save the template.

• **AI model:** Driven by a machine learning algorithm. Training algorithms are usually run over the night, so the model is ready for the next day.

• **Personalized:** The model searches for behavioral similarities between customers and offers **items tailored to each individual customer**. These models require more events for analysis (at least a few months timeframe).

• **Non-personalized:** Model searches for the items that are **shown to all customers**. Such a model can be driven by events (like "top-selling products") or by catalog metadata (gender, categories, color...).

# Simple Non-Personalized Templates

## Popular Right Now

## New Items

## More Like This

## Filter Based

# Advanced Non-Personalized Templates

## Customers Who Bought This Item Also Bought

## Metric-Based Category

## Text Model

# Simple Personalized Templates

A group of personalized recommendation models that are easy to set up.

## Customer Recent Interactions

## Homepage

## Product Detail

# Advanced Personalized Templates

## Personalized Recommendations for You

• **If the item ID is included in the request** the AI model finds the top similar products to the requested product and ranks them by their similarity score. The similarity score is calculated using their shared events (bought, added to the cart, and viewed together). If there are too few shared interactions, the model fallbacks to the “Popular right now” technique where popularity is computed from interaction events defined in the template.

• **If item ID is NOT included in the request** the user’s interaction history (purchases, views, cart updates) is considered, and the AI model finds top similar users. Within similar users, the model looks for items with the highest probability of interaction for a requested user and returns them as recommendations. If there are too few historical interactions, the model retrieves the last three viewed products from the App and runs the **“If included”** steps for these items. Recommendations are then sorted from newest to oldest according to the retrieved products. If there is no historical purchase at all, the “Popular right now” fallback is used with the popularity given as the event with the most interactions.

## Customers Who Viewed This Item Also Viewed

• **If the item ID is included in the request** the AI model finds the top co-occurred products based on the user’s session and sorts them by relevance. If there are too few historical views, the model will recommend the "Most viewed products".

• **If the item ID is NOT included in the request** the user’s last 3 viewed products tracked by Bloomreach Engagement are considered and the `if included` steps are run for these items. Recommendations are then sorted chronologically according to the retrieved products. If there is no historically viewed item, the model will recommend the "Most viewed products".

# Fallback Templates in the [Advanced Template](🔗)

These are models intended as a fallback (support model in case the main one fails to personalize) while you can design a combination of models in the **Advanced template**.

## Manual Selection

## Chosen by Metric

# Recommendation Templates Requirements

Refer to these quick-reference charts to help you understand

• [The template type](🔗),

• [Whether the template is personalized](🔗),

• [Whether Data Mapping is needed](🔗),

• [Which events are needed](🔗),

• [Which catalog attributes are needed](🔗),

• [Which request parameters are needed](🔗).

## Type, Personalization, and Data Mapping

📘



[Data Mapping ](🔗)is defined in the Data Manager and maps the event structure to common product-related actions like views, purchases, add-to-cart, categories, wishlists...

## Product Events Needed

📘



The exact names of the events are not important. A different event name can be used, but its purpose must be equivalent.

'Any' means any product-related event can be used.

## Product Catalog Fields Needed

## Request Parameters Needed

📘



When you request the recommendation using Jinja, JavaScript or the API, it may be necessary to pass "seed" items or category names.