+user On Thu, Feb 26, 2015 at 2:26 PM, Sean Owen <so...@cloudera.com> wrote:
> I think I may have it backwards, and that you are correct to keep the 0 > elements in train() in order to try to reproduce the same result. > > The second formulation is called 'weighted regularization' and is used for > both implicit and explicit feedback, as far as I can see in the code. > > Hm, I'm actually not clear why these would produce different results. > Different code paths are used to be sure, but I'm not yet sure why they > would give different results. > > In general you wouldn't use train() for data like this though, and would > never set alpha=0. > > On Thu, Feb 26, 2015 at 2:15 PM, lisendong <lisend...@163.com> wrote: > >> I want to confirm the loss function you use (sorry I’m not so familiar >> with scala code so I did not understand the source code of mllib) >> >> According to the papers : >> >> >> in your implicit feedback ALS, the loss function is (ICDM 2008): >> >> in the explicit feedback ALS, the loss function is (Netflix 2008): >> >> note that besides the difference of confidence parameter Cui, the >> regularization >> is also different. does your code also has this difference? >> >> Best Regards, >> Sendong Li >> >> >> 在 2015年2月26日，下午9:42，lisendong <lisend...@163.com> 写道： >> >> Hi meng, fotero, sowen: >> >> I’m using ALS with spark 1.0.0, the code should be: >> >> https://github.com/apache/spark/blob/branch-1.0/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala >> >> I think the following two method should produce the same (or near) result: >> >> MatrixFactorizationModel model = ALS.train(ratings.rdd(), 30, 30, 0.01, -1, >> 1); >> >> MatrixFactorizationModel model = ALS.trainImplicit(ratings.rdd(), 30, 30, >> 0.01, -1, 0, 1); >> >> the data I used is display log, the format of log is as following: >> >> user item if-click >> >> >> >> >> >> >> I use 1.0 as score for click pair, and 0 as score for non-click pair. >> >> in the second method, the alpha is set to zero, so the confidence for >> positive and negative are both 1.0 (right?) >> >> I think the two method should produce similar result, but the result is : >> the second method’s result is very bad (the AUC of the first result is >> 0.7, but the AUC of the second result is only 0.61) >> >> >> I could not understand why, could you help me? >> >> >> Thank you very much! >> >> Best Regards, >> Sendong Li >> >> >> >
