Frequently Bought Together

Navigate to 'Playground' in the left hand menu and then select the recommender logic you wish to use - in this case ' Frequently Bought Together'.

Once selected, the recommender configuration options will appear:


 If you wish to use frequently bought together for a specific user, insert a user id. We know the previous purchases for the userid and can recommend products most frequently       purchased with those. The inclusion of a userid also eliminates previous purchased products from the recommendations. 

 Item ID

  You can input an item ID to get items that are also viewed with that item. 

    If you click 'SELECT ITEM FROM CATALOG' you will be taken to a search area where you can search for products. There is also the option to select a product from a list of popular products. Clicking on a product will select that ID for your Frequently Bought Together recommender:

    Select Attributes

    Now you can select the attributes you want to include in the API response - typically these are everything you want to include in your design in your email or on site. Standard fields include:

    - Product image

    - Product title

    - Product url

    - Price

    - Sale price

    - Quantity / availability

    - Gender / category information


    You can apply filters to the recommender to filter out any unwanted products. For example, you may want to only show products from 'Womenswear' or filter out sale items. For more information about filtering, click here.

    Running the Recommender

    Once you have chosen a product ID for the recommender, click on the blue play button to run the recommender: The top 10 products most frequently purchased with the chosen product ID will display:

    Saving your Recommender

    Once you are happy with your configuration, click on the 'CREATE RECOMMENDER' green button. You will see a popup asking for a description for your recommender. Insert a description and click save - this will create your recommender. 

    The recommender will available in the 'Recommenders' area of the platform where you  can export it into the Litmus Personalize platform as a content source. 

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