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Stephanie deWet, Software Engineer, Pinterest at MLconf SF 2016

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The document describes Pinterest's approach to blending content from different pools (recommendations, social, etc.) in a personalized home feed. It uses a multi-armed bandit model where each arm represents a content pool. For each user, it calculates a utility value for each pool based on the user's prior actions. It maps these utilities to integer ratios to determine the blending proportion. For new users, it enforces an initial blend across all pools. An A/B test showed the model increased positive user engagement while allowing control over the social vs. recommendations proportion.

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Personalizedcontentblending
 InthePinteresthomefeed StephaniedeWet,
 PinterestEngineer Pinterest
Whatisahomefeed? Blendingmodel. Agenda 1 2
Whatisa
 homefeed? 1
Pinterestisa personalized catalogofideas.
men’s style Jacob Hinmon End Clothing Men’s blue jacket PinAvisualbookmark savedfromthe internetbyauser.
Weserve
 morethan3trillion Pinseveryyear,
 orabout
 10billionaday.
Followedtopics
Followedusers
Recommendations
Theresultis…
Recommendation Social Newcontent Howdoesthe
 homefeedwork?
Recommendations Ranking Model Social Connections Ranking Model Source Source Recommendation Social Newcontent Howdoesthe
 homefeedwork?
Howdoesthe
 homefeedwork? Recommendations Ranking Model Social Connections Ranking Model Source Recspool Social poolSource Recommendation Social Newcontent
Contentranking Wehavedifferentmodelsforeachtypeofcontent.
Contentranking Wehavedifferentmodelsforeachtypeofcontent. Why?
Contentranking Wehavedifferentmodelsforeachtypeofcontent. Why? • Parallelizesmodeldevelopment
Contentranking Wehavedifferentmodelsforeachtypeofcontent. Why? • Parallelizesmodeldevelopment • Differentcontenttypesmayhavedifferentimportantfeatures
Contentranking Wehavedifferentmodelsforeachtypeofcontent. Why? • Parallelizesmodeldevelopment • Differentcontenttypesmayhavedifferentimportantfeatures • Differentcontenttypesmayhavedifferentobjectivefunctions
Contentranking Wehavedifferentmodelsforeachtypeofcontent. Why? • Parallelizesmodeldevelopment • Differentcontenttypesmayhavedifferentimportantfeatures • Differentcontenttypesmayhavedifferentobjectivefunctions • Easytoaddnewtypesofcontent
Contentranking Wehavedifferentmodelsforeachtypeofcontent. Why? • Parallelizesmodeldevelopment • Differentcontenttypesmayhavedifferentimportantfeatures • Differentcontenttypesmayhavedifferentobjectivefunctions • Easytoaddnewtypesofcontent • Workswellinpractice
Theblendingproblem Recspool Social pool Blender ? homefeed Goal:Combinetheresultsfromthepoolsintoonesortedfeed.
Theblendingproblem Goal:Combinetheresultsfromthepoolsintoonesortedfeed.
Theblendingproblem Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints
Theblendingproblem Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool.
Theblendingproblem Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools.
Theblendingproblem Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools. Desiredtraits
Theblendingproblem Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools. Desiredtraits • Userswithsignificantpriorhistoryshouldseemoreofthecontenttypestheyprefer.
Theblendingproblem Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools. Desiredtraits • Userswithsignificantpriorhistoryshouldseemoreofthecontenttypestheyprefer. • Weshouldbeabletocontroltheblendingrateforuserswithlittlepriorhistory;theseusers shouldseesomeofalltypesofcontent.
Blending model 2
Whichmodel?
Whichmodel? • Fixedratio
Whichmodel? • Fixedratio • Calibraterankingmodels
Whichmodel? • Fixedratio • Calibraterankingmodels • Multi-armedbanditapproach
Model:Multi-armedbandit Eacharmofthebanditrepresentsoneofthepools. Recommendations Friends
Samplingtechnique • Unliketheclassicalbanditproblem,wedonotget feedbackaftereverysample. • Instead,wewillsamplecontentinbatches. • Ineachround,wewillmapthepoolaffinitiesonto anintegerratio.
Data Foreachuserandpool,wehavehistoricengagementdata. Totalnumberof: • PositiveActions(Clickthroughs,Repins,Closeups,…) • NegativeActions(Hides) • Views
Betadistribution Beta(5, 10) Beta(10, 5) Beta(50, 50) E(Beta) = # actions / # views
Responsedistribution Weshouldrepresenteachtypeofactionwithitsownbeta distribution,anditsownreward. Wehavealinearcombinationofresponsesfromeachbandit. + … + rhide *rsave * Recs HideSave Friends Recs Friends
Responsedistribution Weshouldrepresenteachtypeofactionwithitsownbeta distribution,anditsownreward. Inessence,wehavealinearcombinationofbandits. Soweendupwiththefullutilityofapool:
Howdidwedo? Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools. Desiredtraits • Userswithsignificantpriorhistoryshouldseemoreofthecontenttypestheyprefer. • Weshouldbeabletocontroltheblendingrateforuserswithlittlepriorhistory;theseusers shouldseesomeofalltypesofcontent.
Priordistribution Foruserswithlittledata,wewanttoenforce that“theyshouldseesomeofeachofthe
 typesofcontentineverypage.” Wecanenforcethiswithadditionalprioractioncounts. Prioractionscanbeselectedtogiveusthedesiredinitial blendfornewusers,andthedesiredrateofchange.
Samplingtechnique • Ineachround,wewillmaptheexpectedvalues
 ofthepoolutilitiesontoanintegerratio.
Howdidwedo? Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools. Desiredtraits • Userswithsignificantpriorhistoryshouldseemoreofthecontenttypestheyprefer. • Weshouldbeabletocontroltheblendingrateforuserswithlittlepriorhistory;theseusers shouldseesomeofalltypesofcontent.
Example Rewards Positive action 1 Negative action -1
Example Prior Values Positive action 50 Negative action 0 View 1000
Pool # Positive Actions # Negative Actions # Views Utility Function Friends 5000 + 50 0 + 0 10000 + 1000 Recommendation s 0 + 50 500 + 0 10000 + 1000 Userwithhistory
Pool Utility Function Expected Utility Mapped To Ratio Friends 0.46 10 Recommendations -0.04 0 Userwithhistory
Userwithhistory
Newuser Pool # Positive Actions # Negative Actions # Views Utility Function Friends 2 + 50 0 + 0 100 + 1000 Recommendations 2 + 50 0 + 0 10 + 1000
Pool Utility Function Expected Utility Mapped To Ratio Friends 0.047 5 Recommendations 0.051 5 Newuser
Newuser
Howdidwedo?
A/Bexperimentresults 4 3 2 0 %Changeofenabledvscontrol Fractionofuserswhotake1+positiveaction 1 Daysintheexperiment 5 10 15 20
A/Bexperimentresults 4 3 2 Daysintheexperiment %Changeofenabledvscontrol Rateofpositiveactionsonviewedcontent 1 0 5 10 15 20
Personalizingtheratio .25 FractionPinsseenfromsocialpool Fractionofusers .4 .3 .2 .1 .4 .3 .2 .1 .50 .75 .25 .50 .75 Control Enabled
Conclusions Thismodelseemssimple,butitworkedincrediblywellinpractice. Whywasitsuccessful? • Tunablevariablesgiveuscontroloverbusinessobjectives. • Easytoaddnewpoolsofcontent. • Didn’trelyonanyinformationfromtherankingmodels.
Thanks!We’rehiring sdewet@pinterest.com
Stephanie deWet, Software Engineer, Pinterest at MLconf SF 2016

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