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From Suneel Marthi <>
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 <>

> I am pretty sure the LLR stuff in UR is based off of this blog post and
> associated paper:
> Accurate Methods for the Statistics of Surprise and Coincidence
> by Ted Dunning
> On Thu, Nov 16, 2017 at 10:26 AM Noelia Osés Fernández <
>> 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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