Tamas, MAHOUT-371 will be able to leverage the existing DistributedLanczosSolver and DistributedRowMatrix (in o.a.m.math.decomposer.hadoop package in core) to do full sparse truncated SVD on the entire user-item matrix already, so that part is taken care of.
-jake On Thu, May 6, 2010 at 11:38 AM, Tamas Jambor <[email protected]>wrote: > that looks interesting, but quite general. I'd be interested to know how he > plans to divide the task that will be distributed. I mean SVD in general > takes the whole user-item matrix, so it will be challenging to find a good > way to divide the task. Papers written on SVD do not discuss this aspect, as > far as I know. > > > On 06/05/2010 18:32, Sean Owen wrote: > >> We're lucky to have a GSoC student implementing this over the summer: >> https://issues.apache.org/jira/browse/MAHOUT-371 >> >> On Thu, May 6, 2010 at 6:28 PM, Tamas Jambor<[email protected]> >> wrote: >> >> >>> I am looking into the problem of distributed SVD for recommender systems. >>> does anyone know whether someone else tried to tackle this problem >>> before? >>> >>> >>> >>
