recommender system: how to compare different scores when calculated individually?
You can continue to use machine learning for this model-combination step too. You're describing a form of ensembling. How about:


set aside some purchases as your validation set V, and keep the remaining as training set TRAIN
for TRAIN, use k-fold cross-validation to create your separate models
use TRAIN again with k-fold cross-validation to ensemble the models together
report performance of the ensembled models on V


Ensemble techniques will take into account the predictive quality of each individual model.