Recommendation Score

The Recommendation Score works as follows:

Each case in the new (test) data is fed to the analysis model one at a time, with one of the transactions (chosen at random) removed.
The model returns a list of recommendations (at most the number of recommendations in the Measured Accuracy Maximum Predictions parameter).
The model is considered successful if the transaction that was removed is on the list of recommendations.
The score is the percentage of cases in which the analysis model was successful.
The parameter Measured Accuracy Maximum Predictions is set at model build time and should correspond to the number of recommendations you plan to show to the user.

Additional resources