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From Bhaskar Bagchi <bhas...@tinyowl.co.in>
Subject Re: Query about recommendation evaluator.
Date Fri, 05 Jun 2015 10:57:58 GMT
Hi Mahmut,

I also implemented an evaluator today  which does a simple split and it is
running pretty fast but it would be great if you could share your code so
that I can work out if I am missing anything. In my implementation I take a
parameter from user which decides the percentage os split say 0.6 would
mean a 60-40 train to test split and them I generate a single model with
the 60 percent train data and use the same model to evaluate each user an
use the 40% test split to validate, i.e. to calculate precision, recall,
etc.

Thanks and regards.

On Fri, Jun 5, 2015 at 4:15 PM, Mahmut Özge Karakaya <mokarakaya@gmail.com>
wrote:

> Hi Bhaskar,
>
> I used to have same problem and implemented a similar evaluator as you have
> mentioned.
> It's really much faster than original version.
> Moreover in my version splitted dataset is an input since I use it for
> several tests.
> I may share the code or we may work on this.
>
> Regards.
>
>
> 2015-06-05 9:51 GMT+03:00 Bhaskar Bagchi <bhaskar@tinyowl.co.in>:
>
> > Hi,
> >
> > I was working with the GenericRecommenderIRStatsEvaluator when I noticed
> > that the GenericRelevantItemsDataSplitter.java class only removes the
> *good
> > user preferences* for the user for which the evaluation is being run and
> > keeps all the other data points and builds the data point for every user
> > separately before evaluation. This makes the loop O(n^2).
> >
> > Why don't we make a single split of data using the percentage provided by
> > the user and build a single recommender model using this split, which can
> > be used to evaluate all the users? This will make the evaluator pretty
> > fast.
> >
> > Can anyone help me with making a single data split for evaluation?
> >
> > --
> > Thanks and Regards
> >
> > Bhaskar Bagchi
> > Data Science Intern
> > TinyOwl
> >
>



-- 
Regards

Bhaskar Bagchi
Data Science Intern
(+91) 9455625419

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