Overview

The Association Rule Application Processor can be used to generate recommendations by applying (association) rules to item sets which are deduced from a list of transactions. To generate rules from a dataset of transactions the Association Rule Generation Processor or the Network Rule Generation Processor can be used.

Input

As input the processor requires a dataset of transactions as well as a list of rules to apply.

The transactions have to come in the form of a longlist containing single-item transactions (one item to one user in each row).

In the picture we use a Custom Input Table Processor to generate a small dataset by hand. The column Cust holds the IDs of the users and the column Product contains the IDs of the items of the transactions.

The rules come with a LHS (left-hand side) describing the items required as precondition and a RHS (right-hand side) containing the conclusion, that is, resulting item recommendations. Additionally, the rules have to come with a confidence to provide a quality measure for each recommendation and need a unique ID that can be referenced in the recommendation.

This example shows rules generated by an Association Rule Generation Processor and extended by an index as ID.

Configuration

To apply rules, which recommend users for items, just select the item column into the customers parameter of the processor and the user column into the item parameter.

Output

The output of the processor is the list of item recommendations to users. As additional information each recommendation is supplemented with the underlying rule and its confidence. If multiple rules lead to the same recommendation, they will be listed individually.

Additional Information

The processor also outputs rules with confidence 1, that is, rules which - in the training data - always hold and thus do not lead to recommendations of new items. This is due to the additional option to apply the rules to new data and users who are not in the training set for whom the rules might produce valid recommendations.

Algorithmic Background

The algorithm collects all items per user into an item set. It then checks, which rules apply to each user and generates a recommendation for every right hand item of the mentioned rules.

The processor will handle multiple transactions of the same item by the same user by ignoring the multitude.

Example

The following example shows how to use the Association Rule Application Processor in combination with other processors to generate recommendations for customers depending on the items they already bought.

As a starting point we here use a Custom Input Table Processor to generate a small dataset by hand.

The dataset is used by the Association Rule Generation Processor to generate rules and item sets. We want to have a look at both outputs to check if we selected a good configuration for the processor.

In order to apply the association rules we need a Association Rule Application Processor. However, the rules need to have an ID to refer to in the output. Therefore, we use Indexing to give each rule a unique ID. Now we can apply rules to the original dataset via the Association Rule Application Processor.

As a final result we receive the recommendations together with the ID of the applied rule and its confidence in the original dataset (which generated the rule).

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Association Rules Generation Processor