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From Ted Dunning <>
Subject Re: Tuning parameters for ALS-WR
Date Wed, 11 Sep 2013 07:19:01 GMT
On Wed, Sep 11, 2013 at 12:07 AM, Sean Owen <> wrote:

> > 2. Do we have to tune the "similarityclass" parameter in item-based CF?
> If
> > so, do we compare the mean average precision values based on validation
> > data, and then report the same for the test set?
> >
> >
> Yes you are conceptually looking over the entire hyper-parameter space. If
> the similarity metric is one of those, you are trying different metrics.
> Grid search, just brute-force trying combinations, works for 1-2
> hyper-parameters. Otherwise I'd try randomly choosing parameters, really,
> or else it will take way too long to explore. You try to pick
> hyper-parameters 'nearer' to those that have yielded better scores.

Or use a real exploration algorithm.  For my favorite (hear that horn
blowing?) see this article on recorded step
The idea is a randomized search, but with something akin to momentum.  This
lets you search nasty landscapes with pretty pretty good robustness and
smooth ones with fast convergence.  The code and theory are simple and
there is an implementation in Mahout.

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