About Product Recommendations (PR)

The Coveo Machine Learning (Coveo ML) Product Recommendations (PR) feature takes 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 your customers' shopping experience by offering them products that suit their profile, context, and buying behaviors. In order 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.

Members with the required privileges can create, manage, and deploy Coveo ML PR models.

Product Recommendation Strategies

User Recommender

When leveraging the User recommender strategy, the model suggests products to the current user based on their general interests. To achieve this, the model learns from users' previous actions, and uses this information to find other customers that share similar browsing patterns. The model then suggests products that have been previously browsed by customers who share similar interests with the current user.

Frequently Bought Together

When leveraging the Frequently bought together strategy, the model suggests products frequently bought with the current product based on the shopping cart of other users. In other words, the model analyzes the items customers often buy together with the product that the user is currently looking at.

EXAMPLE

As a user of an electronics retailer commerce website, you are currently viewing a gaming computer. Based on other customers' purchase history, the model suggests gaming mice and keyboards that were bought together with the computer you are currently viewing.

Frequently Bought Together in Same Category

When leveraging the frequentBoughtSameCategory strategy, the model recommends the most frequently purchased products in the same category as the item the user is currently viewing.

EXAMPLE

As a user of an electronics retailer commerce website, you are currently viewing a pair of headphones whose category value is headphones.

Since the product page incorporates a recommendations interface that leverages the frequentBoughtSameCategory strategy, the recommended products are the items from the headphones category that have been purchased the most with the item you are currently viewing.

The frequentBoughtSameCategory strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the frequentBoughtSameCategory strategy in your query pipeline, you must associate your model via a JSON configuration.

Frequently Bought Together in Different Categories

When leveraging the frequentBoughtDifferentCategory strategy, the model recommends the products that have been purchased the most with the product that the user is currently viewing. The recommendations are filtered to show products that have a different category than the one the user is currently viewing.

EXAMPLE

As a user of an electronics retailer commerce website, you are currently viewing a gaming computer whose category value is computer.

Since the product page incorporates a recommendations interface that leverages the frequentBoughtDifferentCategory strategy, the recommended products are the items that have been purchased the most with the computer you are currently viewing, but that doesn’t have computer as the category value.

The frequentBoughtDifferentCategory strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the frequentBoughtDifferentCategory strategy in your query pipeline, you must associate your model via a JSON configuration.

Frequently Viewed Together

When leveraging the Frequently viewed together strategy, the model suggests items related to each of the current products based on other users` interactions with your commerce implementation in a single visit. Therefore, this strategy analyzes users` navigation behavior to provide the user with a selection of similar products that are often seen together in a single shopping session.

EXAMPLE

As a user of an electronic retailer commerce website, you are currently viewing the ABC gaming computer. Based on the interactions made by other users that have browsed the same computer in their shopping session, the model suggests products that have frequently been seen together with that computer in the same shopping session.

Since other users have also browsed the 123 and DEF computers along with the ABC gaming computer that you are currently viewing, the model suggests these computers to you.

Frequently Viewed Together in the Same Category

When leveraging the frequentViewedSameCategory strategy, the model recommends the most frequently viewed products in the same category as the item the user is currently viewing.

EXAMPLE

As a user of an electronics retailer commerce website, you are currently viewing a pair of headphones whose category value is headphones.

Since the product page incorporates a recommendations interface that leverages the frequentViewedSameCategory strategy, the recommended products are the items from the headphones category that have been viewed the most with the item you are currently viewing.

The frequentViewedSameCategory strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the frequentViewedSameCategory strategy in your query pipeline, you must associate your model via a JSON configuration.

Frequently Viewed Together in Different Categories

When leveraging the frequentViewedDifferentCategory strategy, the model recommends the products that have been viewed the most with the product that the user is currently viewing. The recommendations are filtered to show products that have a different category than the one the user is currently viewing.

EXAMPLE

As a user of an electronics retailer commerce website, you are currently viewing a gaming computer whose category value is computer.

Since the product page incorporates a recommendations interface that leverages the frequentViewedDifferentCategory strategy, the recommended products are the items that have been viewed the most with the computer you are currently viewing, but that doesn’t have computer as the category value.

The frequentViewedDifferentCategory strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the frequentViewedDifferentCategory strategy in your query pipeline, you must associate your model via a JSON configuration.

Cart Recommender

When leveraging the Cart recommender strategy, the model analyzes frequent buying patterns by grouping sets of products that are frequently purchased together in the same shopping cart.

Given a cart, it will recommend products that were frequently bought together in previous similar carts. This strategy is ideal for cross-selling items on a shopping cart interface.

EXAMPLE

As a user of an electronics retailer commerce website, you are currently viewing your cart where you have a TV and a TV stand. Based on other customers’ purchase history, the model suggests TV accessories, such as HDMI cables, that were bought together with the same TV and TV stand you have in your cart.

When leveraging the Popular items (bought) strategy, the model recommends the most purchased products.

This strategy is useful when you want to display recommendations on a landing page and also used as a backup solution for other strategies when they didn’t gather enough data to provide relevant recommendations.

When leveraging the Popular items (viewed) strategy, the model recommends the most viewed products.

This strategy is useful when you want to display recommendations on a landing page and also used as a backup solution for other strategies when they didn’t gather enough data to provide relevant recommendations.

When leveraging the popularBoughtSameCategory strategy, the model recommends the most purchased products in the same category as the item the user is currently viewing.

This strategy is useful as a backup solution for other strategies when they didn’t gather enough data to provide relevant recommendations.

The popularBoughtSameCategory strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the popularBoughtSameCategory strategy in your query pipeline, you must associate your model via a JSON configuration.

When leveraging the popularViewedSameCategory strategy, the model recommends the most viewed products in the same category as the item the user is currently viewing.

This strategy is useful as a backup solution for other strategies when they didn’t gather enough data to provide relevant recommendations.

The popularViewedSameCategory strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the popularViewedSameCategory strategy in your query pipeline, you must associate your model via a JSON configuration.

Recently Viewed

When leveraging the recentlyViewed strategy, the model analyzes the user’s action history to recommend items recently viewed by the user.

The recentlyViewed strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the recentlyViewed strategy in your query pipeline, you must associate your model via a JSON configuration and ensure that the required user profile dimensions are enabled.

Recently Bought

When leveraging the recentlyBought strategy, the model analyzes the user’s action history to recommend items recently bought by the user.

The recentlyBought strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the recentlyBought strategy in your query pipeline, you must associate your model via a JSON configuration and ensure that the required user profile dimensions are enabled.

Bought Again

When leveraging the boughtAgain strategy, the model recommends items already purchased by the user and ranks these items according to the probability the product will be repurchased.

EXAMPLE

As a user of a commerce website, you previously bought dishwasher detergent, dryer sheets, and fabric softener.

Since the website incorporates a recommendations interface that leverages the boughtAgain strategy, the model recommends the same items you previously bought because those products are likely to be purchased again.

The boughtAgain strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the boughtAgain strategy in your query pipeline, you must associate your model via a JSON configuration and ensure that the required user profile dimensions are enabled.

Recommendations Based on User Affinity

When leveraging the userAffinity strategy, the model recommends items based on the user’s last actions. These recommendations are filtered to provide products that are part of the user’s preferred category or brand.

EXAMPLE

As a user of an electronics retailer commerce website, you recently viewed several computers made by the ACME brand.

Since the home page incorporates a recommendations interface that leverages the userAffinity strategy, the recommended items are the products that have been the most viewed by other users along with the computers you recently viewed.

Since the model detected that you have a preference for the computer category and ACME brand, the provided recommendations are filtered to provide other computers, or other products made by the ACME brand.

The userAffinity strategy cannot be selected from the Coveo Administration Console when associating a Coveo ML PR model with a query pipeline.

To use the userAffinity strategy in your query pipeline, you must associate your model via a JSON configuration and ensure that the required user profile dimensions are enabled.

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