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 = 1
iter 20 =>
MAP_in = 0.035399855240788425
MAP_out = 0.007907455900941737
EPR_in = 0.4902389595686534
EPR_out = 0.4931204751436468
iter 40 =>
MAP_in = 0.033210624652830374
MAP_out = 0.007158070987320343
EPR_in = 0.4907502816419743
EPR_out = 0.49214166351173705
# #factor = 50, alpha = 1, iter = 30
lambda = 1, =>
MAP_in = 0.029096938174350682
MAP_out = 0.006634856811818636
EPR_in = 0.4928298931862564
EPR_out = 0.49328834081999423
lambda = 0.001 =>
MAP_in = 0.02903970778838223
MAP_out = 0.006569378517284138
EPR_in = 0.4929466287464198
EPR_out = 0.49337539845412665
I have not tried other metrics, as said before, RMSE is not that good. I
will give AUC and ROC a try.
I listed some code snippets here. There are 2 evaluation methods and the
main
https://gist.github.com/coderh/05a83be081c1f713e15b
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