A recommendation engine makes intelligent guesses as to what a customer may want to buy based on previous lists of products, which has been made famous by leaders such as Amazon. These lists may be from a current selection within the context of the current session. The list of products may be from previous purchases by the particular customer, and it may even simply be the products that the customer has viewed within a given session. Whichever approach you choose, the training data and scoring data during operational phases must follow the same principles.

In this recipe, we will use the association rules model from the previous recipe to create a recommendation engine. The concept behind the engine is that lists are supplied as asynchronous inputs and recommendations are forwarded as asynchronous outputs where applicable.