Recommenders overview

An over view of the different recommenders available.
LYNX's prediction engine provides valuable recommendations that react in realtime to your customer's shopping journey, leveraging both customer behaviour and product details. There are a number of recommenders available:
Based on user's past behaviour. Recommends items that are most likely to spark the interest of a given user {userId}

Similar items

Recommends items that are similar to an item ID {itemId}. The returned items are sorted by similarity. Item attributes are extracted from the product feed and then LYNX uses this data to find 'similar' items.

Frequently bought together items

Recommends items that are typically complementary to item id {itemId}, or have been purchased at the same time, on the same order, by a large number of other customers.

Top in category items

Recommends items that are the most popular in a given category. Popularity can be based on one of the following metrics: most viewed, most purchased, or highest rated.

Top purchased items

Recommends the most valuable items to a given user {userId} among those that have been purchased the most in a certain period of time.

Also viewed items

Recommends items that are related to the item that's being/been viewed. People that viewed item A, also viewed these other items.

Recently viewed items

Recommends the user's {userId} recently viewed items.

Recently purchased items

Recommend the items that have recently been purchased on your webstore.

Recently added to cart items

Recommends items that have recently been added to users' carts.