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From Dmitriy Lyubimov <dlie...@gmail.com>
Subject Re: Recommeding on Dynamic Content
Date Tue, 01 Feb 2011 21:49:41 GMT
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.
> > > >
> > >
> >
>

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