Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/597#issuecomment-45339833
Simple RMSE is not a great metric for this model, because it treats all
errors equally when the model itself does not at all. 1s are much more
important than 0s. The predictions are not rating-like. See my comment above.
I usually try to look at metrics that measure how good the top of the
ranking is, since this is far more like what the user experiences. MAP or
something like area under the curve are about as good as you can hope for, but
still somewhat flawed. It's hard to eval recommenders since you have such
incomplete information on what the "right" or "relevant" items are.
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