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From Sebastian Schelter <...@apache.org>
Subject Re: Persisting trained models in Mahout
Date Fri, 09 Dec 2011 14:20:12 GMT
Yes, you describe it perfectly. I think the only reason this has not
been done yet is that the model computation is not very fast on Hadoop
because of its iterative nature.

Would you like to work on integrating the SVD recommenders?

--sebastian

On 09.12.2011 11:17, Jens Grivolla wrote:
> On 12/08/2011 03:19 PM, Sebastian Schelter wrote:
>> [...]
>>
>> A model for recommenders that use matrix factorization consists of the
>> user and item feature vectors. You can use a FilePersistenceStrategy
>> with any SVDRecommender to read and write these.
>>
>> In the future we could also support loading the results of
>> ParallelALSFactorizationJob into an SVDRecommender.
> 
> I was actually looking for this. I guess this is the one case where
> there actually is a "model", and calculating the factorization can be
> costly.
> 
> I would expect that doing the "SVD" offline (e.g. on Hadoop) and then
> providing online recommendations which only need a simple linear
> projection is a pretty common use case, isn't it?  You can even take new
> user preferences into account in realtime (when projecting the user
> vector into the feature space) with very little cost, and just update
> the transformation matrices (which should be quite static) periodically.
> 
> Bye,
> Jens
> 


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