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From Sean Owen <>
Subject Re: Evaluation of Mahout recommenders
Date Sat, 27 Oct 2012 10:19:47 GMT
I didn't go but yes it's a nice write-up:

   - The code used is old (2 years / 0.4) but that should not be a
   significant issue
   -  I didn't understand the bit about having to change it to center data,
   since the implementation already does (provides both centered and
   uncentered versions)
   - Centering didn't matter much on this data, and I wonder if that's
   because in fact the "uncentered" implementation was actually centered? I
   don't know
   - Weighting is this made-up way to give more weight to item-item
   similarity based on more data points, which patches up undesirable behavior
   of something like Pearson here. It weights by (1-count/numItems), which is
   not super principled; should have been based on standard deviation of the
   series. I am not sure I agree with arbitrarily changing the weight to
   "count/50" and capping to 1. Just seems even more arbitrary.
   - Weighting helps out Pearson, yes indeed
   - A conclusion of the paper was that they'd found tweaks to improve the
   baseline performance... but the baseline performance is never shown here,
   and the default is to center, and to use weighting. Since the modified
   centering and weighting was the best approach in the graphs, I do wonder
   whether the defaults would have done as well. I assume not (?) for this
   data set. But probably should have been included.
   - The overall analysis of a tradeoff between 'coverage' and accuracy is
   a good and useful one
   - The biggest problem identified with this neighborhood approach is
   sparsity. Indeed you can't make predictions for a lot of items and that's
   generally bad.

On Sat, Oct 27, 2012 at 9:09 AM, Lance Norskog <> wrote:
> Did any of you go to this?
> RUE 2012 – Workshop on Recommendation Utility Evaluation: Beyond RMSE
> One of the poster sessions was an evaluation of the Mahout recommender:
> Case Study Evaluation of Mahout as a Recommender Platform

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