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From Suneel Marthi <smar...@apache.org>
Subject Re: Log-likelihood based correlation test?
Date Thu, 16 Nov 2017 15:59:53 GMT
Indeed so. Ted Dunning is an Apache Mahout PMC and committer and the whole
idea of Search-based Recommenders stems from his work and insights.  If u
didn't know, the PIO UR uses Apache Mahout under the hood and hence u see
the LLR.

On Thu, Nov 16, 2017 at 3:49 PM, Daniel Gabrieli <dgabrieli@salesforce.com>
wrote:

> I am pretty sure the LLR stuff in UR is based off of this blog post and
> associated paper:
>
> http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html
>
> Accurate Methods for the Statistics of Surprise and Coincidence
> by Ted Dunning
>
> http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5962
>
>
> On Thu, Nov 16, 2017 at 10:26 AM Noelia Osés Fernández <
> noses@vicomtech.org> wrote:
>
>> Hi,
>>
>> I've been trying to understand how the UR algorithm works and I think I
>> have a general idea. But I would like to have a *mathematical
>> description* of the step in which the LLR comes into play. In the CCO
>> presentations I have found it says:
>>
>> (PtP) compares column to column using
>> *log-likelihood based correlation test*
>>
>> However, I have searched for "log-likelihood based correlation test" in
>> google but no joy. All I get are explanations of the likelihood-ratio test
>> to compare two models.
>>
>> I would very much appreciate a math explanation of log-likelihood based
>> correlation test. Any pointers to papers or any other literature that
>> explains this specifically are much appreciated.
>>
>> Best regards,
>> Noelia
>>
>

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