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From Sebastian Schelter <...@apache.org>
Subject Re: Recommeding on Dynamic Content
Date Tue, 01 Feb 2011 10:02:51 GMT
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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