Thanks for quick responses. Yes it's that dataset. What I'm using is triplets of "user_id song_id play_times", of ~ 1m users. No audio things, just plein text triples.
It seems to me that the paper about "implicit feedback" matchs well this dataset: no explicit ratings, but times of listening to a song. Thank you Sean for the alpha value, I think they use big numbers is because their values in the R matrix is big. 2013/3/18 Sebastian Schelter <ssc.o...@googlemail.com> > JU, > > are you refering to this dataset? > > http://labrosa.ee.columbia.edu/millionsong/tasteprofile > > On 18.03.2013 17:47, Sean Owen wrote: > > 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 > >> > > > > -- *JU Han* Software Engineer Intern @ KXEN Inc. UTC - Université de Technologie de Compiègne * **GI06 - Fouille de Données et Décisionnel* +33 0619608888
