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From Gökhan Çapan <gkhn...@gmail.com>
Subject Re: Recommeding on Dynamic Content
Date Tue, 01 Feb 2011 11:48:34 GMT
Thanks Sean, Sebastian.

Sean,
a "most similar items" functionality is also critical, so a user based
recommendation approach is not an option for me. I will read the paper you
have suggested.

Sebastian,

Currently I am clustering the items using MinHash, call the clusters as item
profiles, and apply collaborative filtering to these item profiles. When a
new item arrives, I incrementally find the profile they are most similar to,
so they are immediately recommended where their item profile is recommended.


I've read Google's. Actually, MinHash idea came from that paper. (I know
they use MinHash to cluster users different from me)

I have also had a look at Feature-Based Recommendation, it seemed it turns
into a content based recommender to me, I haven't read it in details,
though.

Anyway, I am also thinking on a feature based representation of products,
which will be a combination of some item-specific features with historical
data.



There is an interesting paper from Yahoo: Personalized recommendation on
dynamic content using predictive bilinear models (
www2009.eprints.org/70/1/p691.pdf)

I think they propose a good model based approach for recommendation, as well
as the evaluation. It looks like it may be modified to an item-based
approach..

I will be glad if I can help, if there is a plan to add a model based
recommender to Mahout.



On Tue, Feb 1, 2011 at 12:02 PM, 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
>>
>>
>


-- 
Gökhan Çapan

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