Evaluation of recommendation systems
There's some confusion about different kinds of output and their corresponding evaluation:

One can consider the top N results as predicted positive, any result lower than N as predicted negative. In this option one can use binary classification evaluation measure: precision, recall, f1-score would be the standard measures in this case.
One can consider the ratings/scores assigned to the full set of results. In this case there are two options:

if the numerical results are comparable, e.g. same kind of rating, then standard regression evaluation measures can be used, for instance RMSE.
if not, then it's still possible to compare the order of the results. Spearman rank correlation is a common evaluation measure in this case.



It seems that in your case you could use either the classification or the ranking evaluation measures. Of course, any of these evaluation methods requires gold standard results in order to compare the predictions against them.