> ## Documentation Index
> Fetch the complete documentation index at: https://kb.aampe.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Recommender System

> A combination of user behavior data with catalog information to deliver personalized product recommendations.

## Introduction

Aampe's recommender system **combines user behavior data with catalog information** to deliver personalized product recommendations in push notifications and surfaces. The system also allows a cool-down period to be set, so if a recommendation is made today, N days can elapse before the recommendation can be used again in a communication. The customer can control how long or short this cool-down period is.

The process starts with catalog data ingestion through CSV files, data aggregators, or cloud buckets, capturing essential product details like IDs, names, prices, and deep-links. This data can then be leveraged using CMS filters to write messages specifically for sub-categories in your catalog. **Aampe offers different recommenders for all your use-case ideas, e.g., abandoned cart, browse abandonment, new products, and more**.

The recommendation engine tracks user consumption patterns and creates an item-to-item transition matrix based on factors like sequence frequency and timing. It generates personalized recommendations by matching user history against this matrix, selecting high-scoring items while avoiding previous recommendations.

Implementation is straightforward - recommendations are inserted into messages through a simple interface during content creation. The system continuously learns from user interactions, improving recommendation accuracy over time while effectively handling both popular and niche products across large catalogs.

## Integration Steps

Our dedicated developers will be with you every step of the way to ensure that the integration is smooth and easy. Here’s a step-by-step overview of the process:

### Prepare your CMS data: Ensure your product catalog includes essential fields such as:

* Item ID/SKU
* Item name
* Item price (Optional for in-depth analytics)
* Other relevant catalog properties you wish to include in your messaging (e.g., brand, category, description)

Depending on the recommender you want to use, we require more information. Example: For abandoned cart messages, we would need details of items in both the add\_to\_cart event and the purchase event to process them using CMS. Our developers will go through the use case with you to define what information is needed per use case.

### Choose your integration method:

* Cloud bucket integration

### Verify the integration: Work with our support team to ensure the data is being correctly received and processed.

### Additional Information

**Content Types:** Aampe's recommender works with various content types, including products, blog posts, articles, lessons, videos, and more. **Support**: Integration support is available for any questions or assistance.