One word of caution, is that there are at least two papers on ALS and they define lambda differently. I think you are talking about "Collaborative Filtering for Implicit Feedback Datasets".
I've been working with some folks who point out that alpha=40 seems to be too high for most data sets. After running some tests on common data sets, alpha=1 looks much better. YMMV. In the end you have to evaluate these two parameters, and the # of features, across a range to determine what's best. Is this data set not a bunch of audio features? I am not sure it works for ALS, not naturally at least. On Mon, Mar 18, 2013 at 12:39 PM, Han JU <ju.han.fe...@gmail.com> wrote: > Hi, > > I'm wondering has someone tried ParallelALS with implicite feedback job on > million song dataset? Some pointers on alpha and lambda? > > In the paper alpha is 40 and lambda is 150, but I don't know what are their > r values in the matrix. They said is based on time units that users have > watched the show, so may be it's big. > > Many thanks! > -- > *JU Han* > > UTC - Université de Technologie de Compiègne > * **GI06 - Fouille de Données et Décisionnel* > > +33 0619608888 >
