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From Noelia Osés Fernández <no...@vicomtech.org>
Subject Re: Log-likelihood based correlation test?
Date Mon, 20 Nov 2017 14:49:20 GMT
Thanks Daniel!

And excuse my ignorance but... how do you inspect the ES index?

On 20 November 2017 at 15:29, Daniel Gabrieli <dgabrieli@salesforce.com>
wrote:

> There is this cli tool and article with more information that does produce
> scores:
>
> https://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html
>
> But I don't know of any commands that return diagnostics about LLR from
> the PIO framework / UR engine.  That would be a nice feature if it doesn't
> exist.  The way I've gotten some insight into what the model is doing is by
> when using PIO / UR is by inspecting the the ElasticSearch index that gets
> created because it has the "significant" values populated in the documents
> (though not the actual LLR scores).
>
> On Mon, Nov 20, 2017 at 7:22 AM Noelia Osés Fernández <noses@vicomtech.org>
> wrote:
>
>> This thread is very enlightening, thank you very much!
>>
>> Is there a way I can see what the P, PtP, and PtL matrices of an app are?
>> In the handmade case, for example?
>>
>> Are there any pio calls I can use to get these?
>>
>> On 17 November 2017 at 19:52, Pat Ferrel <pat@occamsmachete.com> wrote:
>>
>>> Mahout builds the model by doing matrix multiplication (PtP) then
>>> calculating the LLR score for every non-zero value. We then keep the top K
>>> or use a threshold to decide whether to keep of not (both are supported in
>>> the UR). LLR is a metric for seeing how likely 2 events in a large group
>>> are correlated. Therefore LLR is only used to remove weak data from the
>>> model.
>>>
>>> So Mahout builds the model then it is put into Elasticsearch which is
>>> used as a KNN (K-nearest Neighbors) engine. The LLR score is not put into
>>> the model only an indicator that the item survived the LLR test.
>>>
>>> The KNN is applied using the user’s history as the query and finding
>>> items the most closely match it. Since PtP will have items in rows and the
>>> row will have correlating items, this “search” methods work quite well to
>>> find items that had very similar items purchased with it as are in the
>>> user’s history.
>>>
>>> =============================== that is the simple explanation
>>> ========================================
>>>
>>> Item-based recs take the model items (correlated items by the LLR test)
>>> as the query and the results are the most similar items—the items with most
>>> similar correlating items.
>>>
>>> The model is items in rows and items in columns if you are only using
>>> one event. PtP. If you think it through, it is all purchased items in as
>>> the row key and other items purchased along with the row key. LLR filters
>>> out the weakly correlating non-zero values (0 mean no evidence of
>>> correlation anyway). If we didn’t do this it would be purely a
>>> “Cooccurrence” recommender, one of the first useful ones. But filtering
>>> based on cooccurrence strength (PtP values without LLR applied to them)
>>> produces much worse results than using LLR to filter for most highly
>>> correlated cooccurrences. You get a similar effect with Matrix
>>> Factorization but you can only use one type of event for various reasons.
>>>
>>> Since LLR is a probabilistic metric that only looks at counts, it can be
>>> applied equally well to PtV (purchase, view), PtS (purchase, search terms),
>>> PtC (purchase, category-preferences). We did an experiment using Mean
>>> Average Precision for the UR using video “Likes” vs “Likes” and “Dislikes”
>>> so LtL vs. LtL and LtD scraped from rottentomatoes.com reviews and got
>>> a 20% lift in the MAP@k score by including data for “Dislikes”.
>>> https://developer.ibm.com/dwblog/2017/mahout-spark-correlated-cross-
>>> occurences/
>>>
>>> So the benefit and use of LLR is to filter weak data from the model and
>>> allow us to see if dislikes, and other events, correlate with likes. Adding
>>> this type of data, that is usually thrown away is one the the most powerful
>>> reasons to use the algorithm—BTW the algorithm is called Correlated
>>> Cross-Occurrence (CCO).
>>>
>>> The benefit of using Lucene (at the heart of Elasticsearch) to do the
>>> KNN query is that is it fast, taking the user’s realtime events into the
>>> query but also because it is is trivial to add all sorts or business rules.
>>> like give me recs based on user events but only ones from a certain
>>> category, of give me recs but only ones tagged as “in-stock” in fact the
>>> business rules can have inclusion rules, exclusion rules, and be mixed with
>>> ANDs and ORs.
>>>
>>> BTW there is a version ready for testing with PIO 0.12.0 and ES5 here:
>>> https://github.com/actionml/universal-recommender/tree/0.7.0-SNAPSHOT Instructions
>>> in the readme and notice it is in the 0.7.0-SNAPSHOT branch.
>>>
>>>
>>> On Nov 17, 2017, at 7:59 AM, Andrew Troemner <atroemner@salesforce.com>
>>> wrote:
>>>
>>> I'll echo Dan here. He and I went through the raw Mahout libraries
>>> called by the Universal Recommender, and while Noelia's description is
>>> accurate for an intermediate step, the indexing via ElasticSearch generates
>>> some separate relevancy scores based on their Lucene indexing scheme. The
>>> raw LLR scores are used in building this process, but the final scores
>>> served up by the API's should be post-processed, and cannot be used to
>>> reconstruct the raw LLR's (to my understanding).
>>>
>>> There are also some additional steps including down-sampling, which
>>> scrubs out very rare combinations (which otherwise would have very high
>>> LLR's for a single observation), which partially corrects for the
>>> statistical problem of multiple detection. But the underlying logic is per
>>> Ted Dunning's research and summarized by Noelia, and is a solid way to
>>> approach interaction effects for tens of thousands of items and including
>>> secondary indicators (like demographics, or implicit preferences).
>>>
>>>
>>> *ANDREW TROEMNER*Associate Principal Data Scientist | salesforce.com
>>> Office: 317.832.4404 <(317)%20832-4404>
>>> Mobile: 317.531.0216 <(317)%20531-0216>
>>>
>>>
>>>
>>> <http://smart.salesforce.com/sig/atroemner//us_mb_kb/default/link.html>
>>>
>>> On Fri, Nov 17, 2017 at 9:55 AM, Daniel Gabrieli <dgabrieli@
>>> salesforce.com> wrote:
>>>
>>>> Maybe someone can correct me if I am wrong but in the code I believe
>>>> Elasticsearch is used instead of "resulting LLR is what goes into the
>>>> AB element in matrix PtP or PtL."
>>>>
>>>> By default the strongest 50 LLR scores get set as searchable values in
>>>> Elasticsearch per item-event pair.
>>>>
>>>> You can configure the thresholds for significance using the
>>>> configuration parameters: maxCorrelatorsPerItem or minLLR.  And this
>>>> configuration is important because at default of 50 you may end up treating
>>>> all "indicator values" as significant.  More info here:
>>>> http://actionml.com/docs/ur_config
>>>>
>>>>
>>>>
>>>> On Fri, Nov 17, 2017 at 4:50 AM Noelia Osés Fernández <
>>>> noses@vicomtech.org> wrote:
>>>>
>>>>>
>>>>> Let's see if I've understood how LLR is used in UR. Let P be the
>>>>> matrix for the primary conversion indicator (say purchases) and Pt its
>>>>> transposed.
>>>>>
>>>>> Then, with a second matrix, which can be P again to make PtP or a
>>>>> matrix for a secondary indicator (say L for likes) to make PtL, we take
a
>>>>> row from Pt (item A) and a column from the second matrix (either P or
L, in
>>>>> this example) (item B) and we calculate the table that Ted Dunning explains
>>>>> on his webpage: the number of coocurrences that item A *AND* B have
>>>>> been purchased (or purchased AND liked), the number of times that item
A
>>>>>  *OR* B have been purchased (or purchased OR liked), and the number
>>>>> of times that *neither* item A nor B have been purchased (or
>>>>> purchased or liked). With this counts we calculate LLR following the
>>>>> formulas that Ted Dunning provides and the resulting LLR is what goes
into
>>>>> the AB element in matrix PtP or PtL. Correct?
>>>>>
>>>>> Thank you!
>>>>>
>>>>> On 16 November 2017 at 17:03, Noelia Osés Fernández <
>>>>> noses@vicomtech.org> wrote:
>>>>>
>>>>>> 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>
wr
>>>>>> ote:
>>>>>>
>>>>>>> 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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>>
>>
>> --
>> <http://www.vicomtech.org>
>>
>> Noelia Osés Fernández, PhD
>> Senior Researcher |
>> Investigadora Senior
>>
>> noses@vicomtech.org
>> +[34] 943 30 92 30 <+34%20943%2030%2092%2030>
>> Data Intelligence for Energy and
>> Industrial Processes | Inteligencia
>> de Datos para Energía y Procesos
>> Industriales
>>
>> <https://www.linkedin.com/company/vicomtech>
>> <https://www.youtube.com/user/VICOMTech>
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>>
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>


-- 
<http://www.vicomtech.org>

Noelia Osés Fernández, PhD
Senior Researcher |
Investigadora Senior

noses@vicomtech.org
+[34] 943 30 92 30
Data Intelligence for Energy and
Industrial Processes | Inteligencia
de Datos para Energía y Procesos
Industriales

<https://www.linkedin.com/company/vicomtech>
<https://www.youtube.com/user/VICOMTech>
<https://twitter.com/@Vicomtech_IK4>

member of:  <http://www.graphicsmedia.net/>     <http://www.ik4.es>

Legal Notice - Privacy policy <http://www.vicomtech.org/en/proteccion-datos>

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