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From: Ted Dunning <ted.dunning@gmail.com>
Date: Tue, 26 Jan 2010 21:15:23 -0800
Message-ID: <c7d45fc71001262115r35062212t977815b0434a290b@mail.gmail.com>
Subject: Re: What is content based recommendation, to you
To: mahout-dev@lucene.apache.org
Cc: mahout-user@lucene.apache.org
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This is a fine way to go (and I have often (mis)used search engines as
recommendation engines).

Another angle is to consider the item level recommendations for a single
item to simply be additional attributes.  You can also look at user level
cooccurrence analysis of attributes (including SVD) as simply a way to
smooth out the attributes a bit so that sparsity doesn't take such a big
bite out of serendipity.

This makes cooccurrence analysis look a whale of a lot like anchor text
propagation which speaks to your final point.



On Tue, Jan 26, 2010 at 6:15 PM, Jake Mannix <jake.mannix@gmail.com> wrote:

> On Tue, Jan 26, 2010 at 3:36 PM, Ted Dunning <ted.dunning@gmail.com>
> wrote:
>
> > I define it a bit differently by redefining recommendations as machine
> > learning.
> >
> > On Tue, Jan 26, 2010 at 1:44 PM, Sean Owen <srowen@gmail.com> wrote:
> >
> > > I would narrow and specify this, in the context of Mahout, to have a
> > > collaborative filtering angle:
> >
>
> Since Ted (Mr. Machine Learning) wants to describe content-based
> recommendations
> as machine learning, and Sean (Mr. Taste/CF) goes and describes it it terms
> of
> collaborative filtering, I suppose I'll put on my "search guy" hat, and
> describe it the
> way I see it:
>



-- 
Ted Dunning, CTO
DeepDyve

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