About Product Recommendations (PR)

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In this article

Prerequisites
Create the PR model
Associate the PR model with a query pipeline
What’s next?

Coveo Machine Learning (Coveo ML) Product Recommendation (PR) models take advantage of Coveo Analytics to suggest relevant products to end users based on their past and present interactions with your Coveo-powered commerce implementation.

Coveo ML PR enhances the user’s experience by offering products that suit their profile, context, and buying behaviors. To provide relevant suggestions, the model continuously learns from your end users' feedback by scoping their buyer profile and analyzing their interactions with different products. Thanks to its multiple algorithms, Coveo ML PR can easily adapt its approach to your digital commerce strategy.

Depending on your context, you can leverage one or more of the available PR strategies.

This article describes the prerequisites needed to create and deploy Coveo ML PR in a Coveo for Commerce interface.

A new version of Coveo Machine Learning (Coveo ML) Product Recommendation (PR) models was released in February 2026. This version is currently in open beta, and a migration path is available for Coveo organizations using the previous version.

Prerequisites

Coveo Machine Learning (Coveo ML) Product Recommendation (PR) models use Coveo Analytics events to relevantly target and suggest products to your visitors. Therefore, you must log commerce events to ensure that your commerce interfaces correctly track user interactions. More specifically, you must log the following event types:

Product views
Purchase events
Cart events (add/remove)
Click events

Notes

PR models will work without cart and click events, but you should still log them for accurate reporting and attribution.
To serve relevant recommendations, a PR model needs at least 10,000 view and/or purchase events to learn from.

Have configured a catalog entity and catalog configuration in your Coveo organization.
Your catalog data for items of the Product catalog object contains data for the ec_category field. Otherwise, category-based PR strategies won’t function correctly. We also recommend that you populate the other commerce standard fields to enhance recommendation precision and diversity.

Create the PR model

Once you ensured that your ecommerce storefronts track the proper usage analytics events, you can create a PR model.

Associate the PR model with a query pipeline

Once your model has been created, you must associate your model with a query pipeline and select an appropriate strategy.

What’s next?

Once your model is associated with a query pipeline, you can then build recommendation interfaces to query the model and display the recommendations in your commerce storefronts.