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Buzz Words Dunning Multi Modal Recommendations
 

Buzz Words Dunning Multi Modal Recommendations

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Multi-model recommendation engines use multiple kinds of behavior as input and can be implemented using standard search engine technology. I show how and why starting with basic recommendations all ...

Multi-model recommendation engines use multiple kinds of behavior as input and can be implemented using standard search engine technology. I show how and why starting with basic recommendations all the way through full multi-modal systems.

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    Buzz Words Dunning Multi Modal Recommendations Buzz Words Dunning Multi Modal Recommendations Presentation Transcript

    • 1©MapR Technologies - Confidential Multi-Modal Recommendations
    • 2©MapR Technologies - Confidential Multiple Kinds of Behavior for Recommending Multiple Kinds of Things
    • 3©MapR Technologies - Confidential What’s Up  What is this multi-modal stuff?  A simple recommendation architecture  Some scary math  Putting it into a deployable architecture  Final thoughts
    • 4©MapR Technologies - Confidential  Contact: – tdunning@maprtech.com – @ted_dunning – @apachemahout – @user-subscribe@mahout.apache.org  Slides and such (available late tonight): – http://www.slideshare.net/tdunning  Hash tags: #bbuzz #mapr #recommendations
    • 5©MapR Technologies - Confidential Recommendations  Often known (inaccurately) as collaborative filtering  Actors interact with items – observe successful interaction  We want to suggest additional successful interactions  Observations inherently very sparse
    • 6©MapR Technologies - Confidential Examples of Recommendations  Customers buying books (Linden et al)  Web visitors rating music (Shardanand and Maes) or movies (Riedl, et al), (Netflix)  Internet radio listeners not skipping songs (Musicmatch)  Internet video watchers watching >30 s (Veoh)
    • 7©MapR Technologies - Confidential What is this multi-modal stuff?  But people don’t just do one thing  One kind of behavior is useful for predicting other kinds  Having a complete picture is important for accuracy  What has the user said, viewed, clicked, closed, bought lately?
    • 8©MapR Technologies - Confidential A simple recommendation architecture  Look at the history of interactions  Find significant item cooccurrence in user histories  Use these cooccurring items as “indicators”  For all indicators in user history, add up scores
    • 9©MapR Technologies - Confidential Recommendation Basics  History: User Thing 1 3 2 4 3 4 2 3 3 2 1 1 2 1
    • 10©MapR Technologies - Confidential Recommendation Basics  History as matrix:  (t1, t3) cooccur 2 times,  (t1, t4) once,  (t2, t4) once,  (t3, t4) once t1 t2 t3 t4 u1 1 0 1 0 u2 1 0 1 1 u3 0 1 0 1
    • 11©MapR Technologies - Confidential A Quick Simplification  Users who do h  Also do r Ah AT Ah( ) AT A( )h User-centric recommendations Item-centric recommendations
    • 12©MapR Technologies - Confidential Recommendation Basics  Coocurrence t1 t2 t3 t4 t1 2 0 2 1 t2 0 1 0 1 t3 2 0 1 1 t4 1 1 1 2
    • 13©MapR Technologies - Confidential Problems with Raw Cooccurrence  Very popular items co-occur with everything – Welcome document – Elevator music  That isn’t interesting – We want anomalous cooccurrence
    • 14©MapR Technologies - Confidential Recommendation Basics  Coocurrence t1 t2 t3 t4 t1 2 0 2 1 t2 0 1 0 1 t3 2 0 1 1 t4 1 1 1 2 t3 not t3 t1 2 1 not t1 1 1
    • 15©MapR Technologies - Confidential Spot the Anomaly  Root LLR is roughly like standard deviations A not A B 13 1000 not B 1000 100,000 A not A B 1 0 not B 0 2 A not A B 1 0 not B 0 10,000 A not A B 10 0 not B 0 100,000 0.44 0.98 2.26 7.15
    • 16©MapR Technologies - Confidential Root LLR Details  In R entropy = function(k) { -sum(k*log((k==0)+(k/sum(k)))) } rootLLr = function(k) { sign = … sign * sqrt( (entropy(rowSums(k))+entropy(colSums(k)) - entropy(k))/2) }  Like sqrt(mutual information * N/2) See http://bit.ly/16DvLVK
    • 17©MapR Technologies - Confidential Threshold by Score  Coocurrence t1 t2 t3 t4 t1 2 0 2 1 t2 0 1 0 1 t3 2 0 1 1 t4 1 1 1 2
    • 18©MapR Technologies - Confidential Threshold by Score  Significant cooccurrence => Indicators t1 t2 t3 t4 t1 1 0 0 1 t2 0 1 0 1 t3 0 0 1 1 t4 1 0 0 1
    • 19©MapR Technologies - Confidential So Far, So Good  Classic recommendation systems based on these approaches – Musicmatch (ca 2000) – Veoh Networks (ca 2005)  Currently available in Mahout – See RowSimilarityJob  Very simple to deploy – Compute indicators – Store in search engine – Works very well with enough data
    • 20©MapR Technologies - Confidential What’s right about this?
    • 21©MapR Technologies - Confidential Virtues of Current State of the Art  Lots of well publicized history – Musicmatch, Veoh, Netflix, Amazon, Overstock  Lots of support – Mahout, commercial offerings like Myrrix  Lots of existing code – Mahout, commercial codes  Proven track record  Well socialized solution
    • 22©MapR Technologies - Confidential What’s wrong about this?
    • 23©MapR Technologies - Confidential Too Limited  People do more than one kind of thing  Different kinds of behaviors give different quality, quantity and kind of information  We don’t have to do co-occurrence  We can do cross-occurrence  Result is cross-recommendation
    • 24©MapR Technologies - Confidential Heh?
    • 25©MapR Technologies - Confidential Symmetry Gives Cross Recommentations Why just dyadic learning? Why not triadic learning?Why not cross learning? AT A( )hBT A( )h
    • 26©MapR Technologies - Confidential For example  Users enter queries (A) – (actor = user, item=query)  Users view videos (B) – (actor = user, item=video)  A’A gives query recommendation – “did you mean to ask for”  B’B gives video recommendation – “you might like these videos”
    • 27©MapR Technologies - Confidential The punch-line  B’A recommends videos in response to a query – (isn’t that a search engine?) – (not quite, it doesn’t look at content or meta-data)
    • 28©MapR Technologies - Confidential Real-life example  Query: “Paco de Lucia”  Conventional meta-data search results: – “hombres del paco” times 400 – not much else  Recommendation based search: – Flamenco guitar and dancers – Spanish and classical guitar – Van Halen doing a classical/flamenco riff
    • 29©MapR Technologies - Confidential Real-life example
    • 30©MapR Technologies - Confidential Hypothetical Example  Want a navigational ontology?  Just put labels on a web page with traffic – This gives A = users x label clicks  Remember viewing history – This gives B = users x items  Cross recommend – B’A = label to item mapping  After several users click, results are whatever users think they should be
    • 31©MapR Technologies - Confidential
    • 32©MapR Technologies - Confidential Nice. But we can do better?
    • 33©MapR Technologies - Confidential Ausers things
    • 34©MapR Technologies - Confidential A1 A2 é ë ù û users thing type 1 thing type 2
    • 35©MapR Technologies - Confidential A1 A2 é ë ù û users action1 item type1 action2 item type2
    • 36©MapR Technologies - Confidential A1 A2 é ë ù û T A1 A2 é ë ù û= A1 T A2 T é ë ê ê ù û ú ú A1 A2 é ë ù û = A1 T A1 A1 T A2 AT 2A1 AT 2A2 é ë ê ê ù û ú ú r1 r2 é ë ê ê ù û ú ú = A1 T A1 A1 T A2 AT 2A1 AT 2A2 é ë ê ê ù û ú ú h1 h2 é ë ê ê ù û ú ú r1 = A1 T A1 A1 T A2 é ëê ù ûú h1 h2 é ë ê ê ù û ú ú
    • 37©MapR Technologies - Confidential Summary  Input: Multiple kinds of behavior on one set of things  Output: Recommendations for one kind of behavior with a different set of things  Cross recommendation is a special case
    • 38©MapR Technologies - Confidential Now again, without the scary math
    • 39©MapR Technologies - Confidential Input Data  User transactions – user id, merchant id – SIC code, amount – Descriptions, cuisine, …  Offer transactions – user id, offer id – vendor id, merchant id’s, – offers, views, accepts
    • 40©MapR Technologies - Confidential Input Data  User transactions – user id, merchant id – SIC code, amount – Descriptions, cuisine, …  Offer transactions – user id, offer id – vendor id, merchant id’s, – offers, views, accepts  Derived user data – merchant id’s – anomalous descriptor terms – offer & vendor id’s  Derived merchant data – local top40 – SIC code – vendor code – amount distribution
    • 41©MapR Technologies - Confidential Cross-recommendation  Per merchant indicators – merchant id’s – chain id’s – SIC codes – indicator terms from text – offer vendor id’s  Computed by finding anomalous (indicator => merchant) rates
    • 42©MapR Technologies - Confidential Search-based Recommendations  Sample document – Merchant Id – Field for text description – Phone – Address – Location
    • 43©MapR Technologies - Confidential Search-based Recommendations  Sample document – Merchant Id – Field for text description – Phone – Address – Location – Indicator merchant id’s – Indicator industry (SIC) id’s – Indicator offers – Indicator text – Local top40
    • 44©MapR Technologies - Confidential Search-based Recommendations  Sample document – Merchant Id – Field for text description – Phone – Address – Location – Indicator merchant id’s – Indicator industry (SIC) id’s – Indicator offers – Indicator text – Local top40  Sample query – Current location – Recent merchant descriptions – Recent merchant id’s – Recent SIC codes – Recent accepted offers – Local top40
    • 45©MapR Technologies - Confidential SolR Indexer SolR Indexer Solr indexing Cooccurrence (Mahout) Item meta- data Index shards Complete history
    • 46©MapR Technologies - Confidential SolR Indexer SolR Indexer Solr search Web tier Item meta- data Index shards User history
    • 47©MapR Technologies - Confidential  Contact: – tdunning@maprtech.com – @ted_dunning – @apachemahout – @user-subscribe@mahout.apache.org  Slides and such (available late tonight): – http://www.slideshare.net/tdunning  Hash tags: #bbuzz #mapr #recommendations  We are hiring!
    • 48©MapR Technologies - Confidential Objective Results  At a very large credit card company  History is all transactions, all web interaction  Processing time cut from 20 hours per day to 3  Recommendation engine load time decreased from 8 hours to 3 minutes  Recommendation quality increased visibly
    • 49©MapR Technologies - Confidential Thank You