Hi, In my experiments with Jellyfish I did not see any substantial RMSE loss over DSGD for Netflix dataset...
So we decided to stick with ALS and implemented a family of Quadratic Minimization solvers that stays in the ALS realm but can solve interesting constraints(positivity, bounds, L1, equality constrained bounds etc)...We are going to show it at the Spark Summit...Also ALS structure is favorable to matrix factorization use-cases where missing entries means zero and you want to compute a global gram matrix using broadcast and use that for each Quadratic Minimization for all users/products... Implementing DSGD in the data partitioning that Spark ALS uses will be straightforward but I would be more keen to see a dataset where DSGD is showing you better RMSEs than ALS.... If you have a dataset where DSGD produces much better result could you please point it to us ? Also you can use Jellyfish to run DSGD benchmarks to compare against ALS...It is multithreaded and if you have good RAM, you should be able to run fairly large datasets... Be careful about the default Jellyfish...it has been tuned for netflix dataset (regularization, rating normalization etc)...So before you compare RMSE make sure ALS and Jellyfish is running same algorithm (L2 regularized Quadratic Loss).... Thanks. Deb On Fri, Jun 27, 2014 at 3:40 AM, Krakna H <shankark+...@gmail.com> wrote: > Hi all, > > Just found this thread -- is there an update on including DSGD in Spark? We > have a project that entails topic modeling on a document-term matrix using > matrix factorization, and were wondering if we should use ALS or attempt > writing our own matrix factorization implementation on top of Spark. > > Thanks. > > > > -- > View this message in context: > http://apache-spark-developers-list.1001551.n3.nabble.com/Spark-Matrix-Factorization-tp55p7097.html > Sent from the Apache Spark Developers List mailing list archive at > Nabble.com. >
