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From Marius Rabenarivo <mariusrabenar...@gmail.com>
Subject Re: LLR and other similarity metrics graph in The Universal Recommender with CCO slide
Date Thu, 04 May 2017 16:07:35 GMT
Thank you

2017-05-04 20:03 GMT+04:00 Pat Ferrel <pat@occamsmachete.com>:

> That was generated using the old Mahout Mapreduce recommenders, which had
> pluggable similarity metrics. I ran it on a vey large E-Commerce dataset
> from a real ecom site. The data was for 6 months of sales. We did
> cross-validation of an 80 training set and 20% held out probe/test set. The
> test set was 20% of the most recent sales. We then measure MAP@k for
> several k. A decline in MAP@k as K increases means the ranking of items
> is correct. This higher MAP@k the better the precision of recommendations.
>
> Using cross-validation between different algorithms is highly suspect so
> this was using an identical algo, but not one I’d use today.
>
>
> On May 4, 2017, at 8:54 AM, Marius Rabenarivo <mariusrabenarivo@gmail.com>
> wrote:
>
> Hello,
>
> Can you point me to some resource explaining how the graphic comparing
> LLR with other similarity metrics was generated?
>
> https://docs.google.com/presentation/d/1MzIGFsATNeAYnLfoR6797ofcLeFRK
> SX7KB8GAYNtNPY/edit#slide=id.g15e36a57f5_0_119
>
> Regards,
>
> Marius
>
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