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From Salil Apte <sa...@offlinelabs.com>
Subject Introducing randomness into my results
Date Fri, 01 Jul 2011 18:42:46 GMT
My first post to the Mahout group. First, Mahout devs, you have created
something great so thanks!

I have inherited some Mahout code and am trying to make some improvements. I
was hoping to get some guidance.

1. We are using the NearestNUserNeighborhood class for neighborhood
calculations. While I want to use the similarity metrics provided in Mahout,
I also want to introduce some randomness. In effect, I want to include a few
people into the final nearest neighbors set that are not actually that
close. That way, my recommender will include some outliers into the results
which is a desirable property for our recommender. What's the best way of
doing this? I can of course implement my own similarity metric (which could
internally use PearsonCorrelationSimilarity) and then randomly give a high
correlation number to certain people. But is there a better way?

2. I also want to introduce some randomness into the final recommended set.
I am thinking I can do this by creating a custom IDRescorer and randomly
bumping up the score for some of the items. This will of course require some
tweaking (how often an item gets a bump, how much of a bump does it get,
etc.) but is there a better way of going about this?

Thanks for the help!

-Salil

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