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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Recommeding on Dynamic Content
Date Tue, 01 Feb 2011 17:02:43 GMT
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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