mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Recommeding on Dynamic Content
Date Tue, 01 Feb 2011 22:16:57 GMT
Ooops.

Yes.  That link is good.  But here is the one that actually illustrates what
I was claiming:

http://lccc.eecs.berkeley.edu/Slides/Weimer_10.pdf

On Tue, Feb 1, 2011 at 1:49 PM, Dmitriy Lyubimov <dlieu.7@gmail.com> wrote:

> The link seems to be a nice summary presentation of the Yahoo paper, same
> authors. Nice.
>
> On Tue, Feb 1, 2011 at 1:09 PM, Ted Dunning <ted.dunning@gmail.com> wrote:
>
> > This looks (based on the first page) very similar to the Menon and Elkan
> > paper.
> >
> > Note that parallel != fast.  The LLL implementation of Menon and Elkan
> > reportedly munches all of netFlix in about 8 minutes if I remember
> > correctly.  Most batch update gradient methods are highly parallelizable,
> > but are slower even after parallelization than sequential SGD
> > implementations.  In Mahout on the relatively small 20 newsgroups, SGD is
> > faster than anything else we have.  This applies to pretty large problem
> > sizes (10's of millions of training examples after stratified
> > down-sampling,
> > billions before).
> >
> > Conversely, just because SGD isn't normally parallelized, doesn't mean it
> > can't be.  See here for a counter-example:
> > http://www.ideal.ece.utexas.edu/seminar/LatentFactorModels.pdf  (thanks
> to
> > Isabel for hooking me up with Markus)
> >
> > On Tue, Feb 1, 2011 at 12:27 PM, Dmitriy Lyubimov <dlieu.7@gmail.com>
> > wrote:
> >
> > > There's also a paper from Yahoo! research "Regression-based Latent
> Factor
> > > Models" http://portal.acm.org/citation.cfm?id=1557029
> > >
> > > What i like about this is that it doesn't focus on a particular method
> to
> > > combine the models to regress on static profile data + side info. I
> think
> > > it
> > > might be combined with methods ALS-WS  which unlke SGD are
> > > hadoop-parallelizable to do stage computations. It also serves pretty
> > good
> > > in situations when there are dyadic interactions but different types
> > > interaction context (side info) are available (or sometimes none at
> all)
> > > but
> > > static profile information is always available. I think we'll have to
> get
> > > on
> > > this problem pretty soon .
> > >
> > >
> > > On Tue, Feb 1, 2011 at 8:24 AM, Ted Dunning <ted.dunning@gmail.com>
> > wrote:
> > >
> > > > And the Mahout-525 github branch of mahout that I started has an
> > > apparently
> > > > working version for this algorithm.
> > > >
> > > > I would love to support anyone who wants to do last mile work on that
> > > > stuff.
> > > >
> > > > See https://issues.apache.org/jira/browse/MAHOUT-525 for more info
> > > >
> > > > On Tue, Feb 1, 2011 at 1:52 AM, Sean Owen <srowen@gmail.com> wrote:
> > > >
> > > > > That Elkan / Menon paper has an elegant theoretical formulation of
> a
> > > > > recommender that uses both ratings and side info at the same time.
> > > > >
> > > >
> > >
> >
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message