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.
--sebastian
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 <ssc@apache.org
> <mailto: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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