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}
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.
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.
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.
Recommends the most valuable items to a given user {userId} among those that have been purchased the most in a certain period of time.
Recommends items that are related to the item that's being/been viewed. People that viewed item A, also viewed these other items.
Recommends the user's {userId} recently viewed items.
Recommend the items that have recently been purchased on your webstore.
Recommends items that have recently been added to users' carts.
Last modified 2yr ago