Yes, this will show the model. But if you do this a lot there are tools like
Restlet that you plug in to Chrome. They will allow you to build queries of all
sorts. For instance
GET http://localhost:9200/urindex/_search?pretty
will show the item rows of the UR model put into the index for the integration
test data. The UI is a bit obtuse but you can scroll down in the right pane
expanding bits of JSON as you go to see this:
"hits":{
"total": 7,
"max_score": 1,
"hits":[
{
"_index": "urindex_1511033890025",
"_type": "items",
"_id": "Nexus",
"_score": 1,
"_source":{
"defaultRank": 4,
"expires": "2017-11-04T19:01:23.655-07:00",
"countries":["United States", "Canada"],
"id": "Nexus",
"date": "2017-11-02T19:01:23.655-07:00",
"category-pref":["tablets"],
"categories":["Tablets", "Electronics", "Google"],
"available": "2017-10-31T19:01:23.655-07:00",
"purchase":[],
"popRank": 2,
"view":["Tablets"]
}
},
As you can see no purchased items survived the correlation test, one survived
the view and category-pref correlation tests. The other fields are item
properties set using $set events and are used with business rules.
With something like this tool you can even take the query logged in the
deployed PIO server and send it to see how the query is constructed and what
the results are (same as you get from the SDK I’ll wager :-)
On Nov 20, 2017, at 7:07 AM, Daniel Gabrieli <dgabri...@salesforce.com> wrote:
There is a REST client for Elasticsearch and bindings in many popular languages
but to get started quickly I found this commands helpful:
List Indices:
curl -XGET 'localhost:9200/_cat/indices?v&pretty'
Get some documents from an index:
curl -XGET 'localhost:9200/<INDEX>/_search?q=*&pretty'
Then look at the "_source" in the document to see what values are associated
with the document.
More info here:
https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-get.html#_source
<https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-get.html#_source>
this might also be helpful to work through a single specific query:
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-explain.html
<https://www.elastic.co/guide/en/elasticsearch/reference/current/search-explain.html>
On Mon, Nov 20, 2017 at 9:49 AM Noelia Osés Fernández <no...@vicomtech.org
<mailto:no...@vicomtech.org>> wrote:
Thanks Daniel!
And excuse my ignorance but... how do you inspect the ES index?
On 20 November 2017 at 15:29, Daniel Gabrieli <dgabri...@salesforce.com
<mailto:dgabri...@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
<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 <no...@vicomtech.org
<mailto:no...@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 <p...@occamsmachete.com
<mailto:p...@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 <http://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/
<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
<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 <atroem...@salesforce.com
<mailto:atroem...@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 <http://salesforce.com/>
Office: 317.832.4404 <tel:(317)%20832-4404>
Mobile: 317.531.0216 <tel:(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 <dgabri...@salesforce.com
<mailto:dgabri...@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
<http://actionml.com/docs/ur_config>
On Fri, Nov 17, 2017 at 4:50 AM Noelia Osés Fernández <no...@vicomtech.org
<mailto:no...@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 <no...@vicomtech.org
<mailto:no...@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 <smar...@apache.org
<mailto:smar...@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 <dgabri...@salesforce.com
<mailto:dgabri...@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
<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
<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 <no...@vicomtech.org
<mailto:no...@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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