Apologies, I'm mixing up 1.x and 2.x. The default evaluation metric in 1.x is mean average precision, or MAP. This is a measure of how much the top recommendations contained some items that were held out for the user. In local mode you can find lines like "Mean average precision: xxx" in the logs.   In distributed mode, now that I review the code, I don't see that it is ever logged. It is written to a file called "MAP" under the subdirectory for the iteration. I can make the mapper workers output their own local value of MAP at least.   In 2.x the metric is AUC, which is basically a measure of how likely it is that a 'good' recommendation (from the held out data set) ranks above a random item. It is a broader, different measure. This you should find printed in the logs if you're using 2.x for sure, along with hyperparams that yielded it. ... View more