This item-based widget enables you to recommend a range of products that are often purchased with a specific product.

By offering complimentary product options, you not only personalize the shopper experience but also improve upsells/cross-sell opportunities for greater revenue.

Frequently Bought Together (FBT) widget leverages the Frequently Bought Together algorithm. This algorithm analyzes behavioral and product data to recommend the products purchased together frequently.

Bloomreach’s Machine learning models leverage a powerful combination of [Neural Networks](🔗) and [Association Rule](🔗) mining. These deep learning models are trained on the past 90 days of behavioral and product data to recognize purchase patterns and make product predictions.

## Algorithm Features

  • By default, browse session data is used by the algorithm. You have the option to aggregate visit session data as well.

  • The algorithm returns up to 250 recommendations.

  • The recommendations are refreshed every 48 hours. This ensures that the product data in recommendations accurately reflect the latest changes in the catalog.

Best Practice

Kindly ensure that the [ATC pixels](🔗) are integrated correctly. This helps generate the right recommendations.

## Algorithm Types

The Frequently Bought Together Algorithm has 2 model variants:

#### **Precise [Default]**

  • Precise FBT algorithm — a tree-based algorithm — only recommends those products that were seen together in the relevant session.

  • The algorithm generates very precise recommendations.

#### **Relaxed**

  • Relaxed FBT algorithm — a neural network-based algorithm— returns more recommendations than the Precise FBT algorithm.

    For example, if a\<=>b was a recommendation and b\<=>c was a recommendation, then it recommends a\<=>c. Suppose users bought pants with a shirt and purchased shoes with pants; then, users can see shoes as recommendations for a shirt search query.

## Available Algorithm Customizations

Please reach out to [Bloomreach Support](🔗) to customize the following as per your needs:

  1. You can select one of the 2 models — Precise and Relaxed.

  2. You can customize the maximum recall size (number of recommendations per PID) generated by the algorithm. This should be between 200-250.

  3. **Minimum threshold for products to be frequently bought together:** This refers to the minimum likelihood that if product P1 is purchased, it will be bought together with product P2. If the number of observed instances is low, this minimum threshold can be lowered.

  4. **Filtering out products based on Add to Cart (ATC) activity**: This is the minimum threshold for the number of times a product needs to be added to the cart in different sessions to be considered for recommendation. If the number of ATC instances falls below this threshold, the product will be excluded from the recommendations. By default, this threshold is set to 5, meaning a product must be added to the cart at least 5 times in different sessions to be considered.

  5. If the algorithm doesn’t generate enough recommendations, you can choose to combine results from the [Similar Products](🔗) algorithm.

  6. You can choose to aggregate browse session data with visit session data.

  7. Our recommendation for FBT is to use visit session data. Suppose a user searches for shirts in a browse session, and in another subsequent browse session they search for pants that go with it, this data will be considered in the recommendation if the visit session is used.