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> 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 <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> 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 <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 >>> 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 < >>>> 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> 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> 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 < >>>>>>>> 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 >>>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>> >>> -- >>> You received this message because you are subscribed to the Google >>> Groups "actionml-user" group. >>> To unsubscribe from this group and stop receiving emails from it, send >>> an email to actionml-user+unsubscr...@googlegroups.com. >>> To post to this group, send email to actionml-u...@googlegroups.com. >>> To view this discussion on the web visit https://groups.google. >>> com/d/msgid/actionml-user/CAA2BRS%2Boj%2BNYDmsNNd2mYM1ZC5CgWwC71W3% >>> 3DEhrO9qeOiKyWXA%40mail.gmail.com >>> <https://groups.google.com/d/msgid/actionml-user/CAA2BRS%2Boj%2BNYDmsNNd2mYM1ZC5CgWwC71W3%3DEhrO9qeOiKyWXA%40mail.gmail.com?utm_medium=email&utm_source=footer> >>> . >>> For more options, visit https://groups.google.com/d/optout. >>> >>> >> >> >> -- >> <http://www.vicomtech.org> >> >> Noelia Osés Fernández, PhD >> Senior Researcher | >> Investigadora Senior >> >> no...@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> >> <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> >> > -- <http://www.vicomtech.org> Noelia Osés Fernández, PhD Senior Researcher | Investigadora Senior no...@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>
