Github user coderh commented on the pull request:
https://github.com/apache/spark/pull/597#issuecomment-45338790
Just a question on the result.
```
implicitPref rank numInterations lambda -> rmse
true 30 40 1.0 -> 0.5776665087027969
```
Here, 0.57 is the error we will make when we predict 0/1, but is that too
much ?
That means the preference we predicted is +/- 0.57 far from 0/1. It doesn't
look good enough for me.
Tell me if I am missing something. And how can I know that what rmse
indicates a good prediction ?
In the paper on which the implicit ALS is based on, we see that it used
expected percentile rank.
Maybe, mean averaged precision at k (MAP@K) will also be useful for
evaluation. Do you have some kind of these results ?
Thank you. =)
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