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From vineet yadav <vineet.yadav.i...@gmail.com>
Subject Re: Recommeding on Dynamic Content
Date Tue, 01 Feb 2011 19:25:03 GMT
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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