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From Sebastian Schelter <>
Subject Re: Recommeding on Dynamic Content
Date Wed, 02 Feb 2011 08:14:23 GMT
Hi Ted,

I looked through the paper a while ago. The approach seems to have great 
potential, especially because of the ability to include side information 
and to work with nominal and ordinal data. Unfortunately I have to admit 
that a lot of the mathematical details overextend my understanding. I'd 
be ready to assist anyone willing to build a recommender from that 
approach but it's not a thing I could tackle on my own.


PS: The algorithm took 7 minutes to learn from the movielens 1M dataset, 
not Netflix.

On 01.02.2011 18:02, Ted Dunning 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 < 
> <>> 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" ( ), 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" (
> ),
>     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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