ARL is a rule based machine learning technique which is used for finding pattern (relation) in a given set. It is a method used to reveal product associations from user purchases. It provides the opportunity to see the combination of products purchased according to a threshold value to be determined.
- Association rules are produced for locations and segments using association analysis and recommendations are made according to these rules.
- The main objective is to find and propose a general recommendation product on the basis of segment and country.
- Rules are expected to be learned from all data and from within each segment, but recommendations are expected to be segment and country specific.
- The rules will be learned from the A segment in the whole data set, but the proposal will be made to the A segment of the relevant country.
- The rules will be learned from the B segment in the whole data set, but the proposal will be made to the B segment of the respective country.
- The rules will be learned from segment C in the whole data set, but the proposal will be made to segment C of the respective country.