About Product Recommendations (PR)

This is for:

System Administrator

Coveo Machine Learning (Coveo ML) Product Recommendation (PR) models take advantage of Coveo Usage Analytics (Coveo UA) 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 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 positive and negative 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.

Prerequisites

Coveo Machine Learning (Coveo ML) Product Recommendation (PR) models use usage analytics (UA) 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

Important

  • PR models will work without cart and click events, but we recommend logging them to improve the model’s accuracy.

  • If your PR model has fewer than 100 events to learn from, it lacks sufficient data to generate recommendations. With at least 100 events, the model begins learning and improving.

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.