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From Gabor Bernat <ber...@primeranks.net>
Subject ALSWR MovieLens 100k
Date Thu, 09 May 2013 12:38:08 GMT
Hello,

So I've been testing out the ALSWR with the Movielensk 100k dataset, and
I've run in some strange stuff. An example of this you can see in the
attached picture.

So I've used feature count1,2,4,8,16,32, same for iteration and summed up
the results in a table. So for a lambda higher than 0.07 the more important
factor seems to be the iteration count, while increasing the feature count
may improve the result, however not that much. And this is what one could
expect from the algrithm, so that's okay.

The strange stuff comes for lambdas smaller than 0.075. In this case the
more important part becames the feature count, hovewer not more but less is
better. Similary for the iteration count. Essentially the best score is
achieved for a really small lambda, and a single feature and iteration
count. How is this possible, am I missing something?


Bernát GÁBOR

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