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From vineet yadav <vineet.yadav.i...@gmail.com>
Subject Re: Recommeding on Dynamic Content
Date Tue, 01 Feb 2011 19:55:53 GMT
Hi Ted,
Yes, In paper they have mentioned the point that "locally optimized
co-clustering gives poor result in iterative learning", so they have used
evolutionary co-clustering that gives better result.
Thanks
Vineet Yadav

On Wed, Feb 2, 2011 at 1:12 AM, Ted Dunning <ted.dunning@gmail.com> wrote:

> 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<http://www.dollar.biz.uiowa.edu/%7Estreet/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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