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From Daniel Gabrieli <dgabri...@salesforce.com>
Subject Re: Log-likelihood based correlation test?
Date Mon, 20 Nov 2017 14:29:33 GMT
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>
>>>>> 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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>
>
> --
> <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>
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