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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Recommeding on Dynamic Content
Date Tue, 01 Feb 2011 19:42:11 GMT
Co-clustering typically doesn't give really hot results (at least in my
reading and experience).

On Tue, Feb 1, 2011 at 11:25 AM, vineet yadav
<vineet.yadav.iiit@gmail.com>wrote:

> Hi Gökhan,
> Also check out paper "Incremental Collaborative Filtering via Evolutionary
> Co-clustering"(
> http://www.dollar.biz.uiowa.edu/~street/research/recsys10_ecoc.pdf), In
> paper, author proposed a method to  use new data in  collaborative
> filtering
> model incrementally. Here co-clustering is used to cluster row and
> column(items and user) simultaneously. Also check master thesis
> "RECOMMENDING
 ARTICLES FOR AN
 ONLINE NEWSPAPER
"  (
> http://www.ilk.uvt.nl/downloads/pub/papers/hait/kneepkens2009.pdf).
> Thanks
> Vineet Yadav
>
> On Tue, Feb 1, 2011 at 10:32 PM, Ted Dunning <ted.dunning@gmail.com>
> wrote:
>
> > Sebastian,
> >
> > Have you read the Elkan paper?  Are you interested in (partially) content
> > based recommendation?
> >
> > On Tue, Feb 1, 2011 at 2:02 AM, Sebastian Schelter <ssc@apache.org>
> wrote:
> >
> > > Hi Gökhan,
> > >
> > > I wanna point you to some papers I came across that deal with similar
> > > problems:
> > >
> > > "Google News Personalization: Scalable Online Collaborative Filtering"
> (
> > > http://www2007.org/papers/paper570.pdf ), this paper describes how
> > Google
> > > uses three algorithms (two of which cluster the users) to achieve
> online
> > > recommendation of news articles.
> > >
> > > "Feature-based recommendation system" (
> > > http://glaros.dtc.umn.edu/gkhome/fetch/papers/fbrsCIKM05.pdf ), this
> > > approach didn't really convince me and I think the paper is lacking a
> lot
> > of
> > > details, but it might still be an interesting read.
> > >
> > > --sebastian
> > >
> > > On 01.02.2011 00:26, Gökhan Çapan 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
> item
> > >> 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
> > related
> > >> papers?
> > >>
> > >> (As an MSc student, I can implement this as a Google Summer of Code
> > >> project,
> > >> with your mentoring.)
> > >>
> > >> Thanks in advance
> > >>
> > >>
> > >
> >
>

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