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From Noelia Osés Fernández <no...@vicomtech.org>
Subject Re: Log-likelihood based correlation test?
Date Thu, 16 Nov 2017 16:03:46 GMT
Wonderful! Thanks Daniel!

Suneel, I'm still new to the Apache ecosystem and so I know that Mahout is
used but only vaguely... I still don't know the different parts well enough
to have a good understanding of what each of them do (Spark, MLLib, PIO,
Mahout,...)

Thank you both!

On 16 November 2017 at 16:59, Suneel Marthi <smarthi@apache.org> wrote:

> 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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