Hi I just subscribed this maillist and plan to use mahout collaborative filtering part. I feel that mahout may be better focused on a few algorithms first and do it very well in a scalable way. Simple algorithms such as naive bayes and {item|user}-based collaborative filtering may be the initial focus. Complex algorithms such as LDA can be delayed. Applying a large data set in simple algorithms, we can achieve a very good quality. This is also where the scalability, one important characteristics of mahout, really matters.
Best, Albert. On Fri, Sep 4, 2009 at 3:21 PM, Grant Ingersoll <gsing...@apache.org> wrote: > > On Sep 4, 2009, at 1:07 PM, Ted Dunning wrote: > > These are good questions to ask. I don't know that we are ready to answer >> them, but I do think that we have pieces of the answers. >> >> So far, there are three or four general themes that seem to be of real >> interest/value >> >> a) taste/collaborative filtering/cooccurrence analysis >> >> b) facilitation of conventional machine learning by large scale >> aggregation >> using hadoop (so far, this is largely cooccurrence counting) >> >> c) standard and basic machine learning tasks like clustering, simple >> classifiers running on large scale data >> >> d) stuff >> > > I'd add a few non-technical things I find useful: > > e) Non-viral License > > f) Community supporting it (i.e. not abandoned) and a place to get answers > about practical problems. > > I've been frustrated more than once by the lack of (e) and (f) on some > other projects. Not that I'm saying we solve (f) yet completely (could use > a bit more diversity in people answering, but that is starting to take hold, > too), but I do firmly believe Apache is one of the best places to build a > community. > > -Grant >