Github user coderh commented on the pull request:
https://github.com/apache/spark/pull/597#issuecomment-45898782
Ok, I have found the error in my metric.
```
val itemFactors = model.productFeatures.collect()
```
This line is for creating a item-factor matrix, the
Github user coderh commented on the pull request:
https://github.com/apache/spark/pull/597#issuecomment-45847264
Here is the values I have tried: seed is set to 42
in & out means in sample (training set) out-of-sample (test set)
# #factor = 12, lamda = 1, alpha
Github user coderh commented on the pull request:
https://github.com/apache/spark/pull/597#issuecomment-45731942
I have tried different lamdba and # features. But nothing has changed. To
be clear, initially, the Movielens dataset it is divided into training set(80%)
and test set(20
Github user coderh commented on the pull request:
https://github.com/apache/spark/pull/597#issuecomment-45640620
I have recently tested expected percentile rank evaluation method proposed
in the paper on the Movielens data set and a real world data set. However, I
got a expected rank
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
```
Github user coderh commented on the pull request:
https://github.com/apache/spark/pull/165#issuecomment-45245775
Hi, @mengxr
You have mentioned that you have tested implicit ALS on the movielens data.
Do you have any evaluation result on that ? I am so curious
