Mailing-List: contact user-help@mahout.apache.org; run by ezmlm
Precedence: bulk
Reply-To: user@mahout.apache.org
Received-SPF: pass (athena.apache.org: domain of ted.dunning@gmail.com
 designates 209.85.160.170 as permitted sender)
DomainKey-Signature: a=rsa-sha1; c=nofws;
        d=gmail.com; s=gamma;
        h=mime-version:in-reply-to:references:from:date:message-id:subject:to
         :content-type;
        b=nrsvwwJQSGXMPxvN0fpOq2tyRj7URsf/Zw+3s5GDf2PTteP3h3y7Rc652PL3z/LX1T
         gzVVth40AqMhb58yrkkK+b2hvi1Ul60fzPp/xxplIjSiVPsvDDANNZYDQxP6A3UllQVA
         8DBiHLPMeuIxWRCAc6kcDR1bzeczJIDYvkhZI=
MIME-Version: 1.0
In-Reply-To: <AANLkTikt3SHTTyDNjoDVhikALctF_OGH5O893d7-Dac_@mail.gmail.com>
References: <AANLkTindfd8XEduJxMdCV0VSRSNdUDhQ2SxX7jzRqR8h@mail.gmail.com>
 <AANLkTikt3SHTTyDNjoDVhikALctF_OGH5O893d7-Dac_@mail.gmail.com>
From: Ted Dunning <ted.dunning@gmail.com>
Date: Tue, 1 Feb 2011 08:26:50 -0800
Message-ID: <AANLkTi=7jZOYpUMxR4GFd7pSvMs1yWe7ObK9FHUXNC=w@mail.gmail.com>
Subject: Re: Recommeding on Dynamic Content
To: user@mahout.apache.org
Content-Type: multipart/alternative; boundary=90e6ba5bc9d97edeb4049b3b02ea

--90e6ba5bc9d97edeb4049b3b02ea
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: quoted-printable

Here is a pointer to the Menon and Elkan paper:
http://arxiv.org/abs/1006.2156

 <http://arxiv.org/abs/1006.2156>Also, see chapter 17 of Mahout in Action
for a description of how you can use the SGD classifiers already in Mahout
for this kind of work.

You lose the very cool recommendations framework that Mahout has, but you
gain the ability to recommend in high churn situations.

On Tue, Feb 1, 2011 at 1:52 AM, Sean Owen <srowen@gmail.com> wrote:

> One approach is to use user-user similarities. Those build up over time
> based on historical data, but can be used to produce recommendations for
> brand-new items going forward.
>
> It still has a cold-start problem; until anyone connects to one of those
> new
> items, it can't be recommended.
>
> Another approach is to use the item's characteristics to determine some
> notion of similarity, in the absence of clicks. That's what you're doing
> and
> it's a great approach.
>
> You can also consider hybrid approaches. You could try to mix
> recommendations based on two different approaches -- clicks-based and
> content-based. The problem is knowing how to mix things since the scores
> are
> not at all comparable.
>
> That Elkan / Menon paper has an elegant theoretical formulation of a
> recommender that uses both ratings and side info at the same time.
>
>
> On Mon, Jan 31, 2011 at 11:26 PM, G=C3=B6khan =C3=87apan <gkhncpn@gmail.c=
om> wrote:
>
> > Hi,
> >
> > I've made a search, sorry in case this is a double post.
> > Also, this question may not be directly related to Mahout.
> >
> > Within a domain which is enitrely user generated and has a very big ite=
m
> > churn (lots of new items coming, while some others leaving the system),
> > what
> > do you recommend to produce accurate recommendations using Mahout (Not
> just
> > Taste)?
> >
> > I mean, as a concrete example, in the eBay domain, not Amazon's.
> >
> > Currently I am creating item clusters using LSH with MinHash (I am not
> sure
> > if it is in Mahout, I can contribute if it is not), and produce
> > recommendations using these item clusters (profiles). When a new item
> > arrives, I find its nearest profile, and recommend the item where its
> > belonging profile is recommended to. Do you find this approach good
> enough?
> >
> > If you have a theoretical idea, could you please point me to some relat=
ed
> > papers?
> >
> > (As an MSc student, I can implement this as a Google Summer of Code
> > project,
> > with your mentoring.)
> >
> > Thanks in advance
> >
> > --
> > Gokhan
> >
>

--90e6ba5bc9d97edeb4049b3b02ea--