Personalized Home Feed Blending Using Multi-Armed Bandit Model
•
7 gostaram•1,467 visualizações
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.
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Destaque
Destaque (20)
Semelhante a Personalized Home Feed Blending Using Multi-Armed Bandit Model
Semelhante a Personalized Home Feed Blending Using Multi-Armed Bandit Model (20)
Mais de MLconf
Mais de MLconf (20)
Último
Último (20)
Personalized Home Feed Blending Using Multi-Armed Bandit Model
- 6. men’s style Jacob Hinmon End Clothing Men’s blue jacket PinAvisualbookmark savedfromthe internetbyauser.
- 10. Recommendations
- 11. Theresultis…
- 14. Howdoesthe homefeedwork? Recommendations Ranking Model Social Connections Ranking Model Source Recspool Social poolSource Recommendation Social Newcontent
- 19. Contentranking Wehavedifferentmodelsforeachtypeofcontent. Why? • Parallelizesmodeldevelopment • Differentcontenttypesmayhavedifferentimportantfeatures • Differentcontenttypesmayhavedifferentobjectivefunctions
- 20. Contentranking Wehavedifferentmodelsforeachtypeofcontent. Why? • Parallelizesmodeldevelopment • Differentcontenttypesmayhavedifferentimportantfeatures • Differentcontenttypesmayhavedifferentobjectivefunctions • Easytoaddnewtypesofcontent
- 21. Contentranking Wehavedifferentmodelsforeachtypeofcontent. Why? • Parallelizesmodeldevelopment • Differentcontenttypesmayhavedifferentimportantfeatures • Differentcontenttypesmayhavedifferentobjectivefunctions • Easytoaddnewtypesofcontent • Workswellinpractice
- 28. Theblendingproblem Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools. Desiredtraits • Userswithsignificantpriorhistoryshouldseemoreofthecontenttypestheyprefer.
- 29. Theblendingproblem Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools. Desiredtraits • Userswithsignificantpriorhistoryshouldseemoreofthecontenttypestheyprefer. • Weshouldbeabletocontroltheblendingrateforuserswithlittlepriorhistory;theseusers shouldseesomeofalltypesofcontent.
- 30. Blending model 2
- 31. Whichmodel?
- 34. Whichmodel? • Fixedratio • Calibraterankingmodels • Multi-armedbanditapproach
- 36. Samplingtechnique • Unliketheclassicalbanditproblem,wedonotget feedbackaftereverysample. • Instead,wewillsamplecontentinbatches. • Ineachround,wewillmapthepoolaffinitiesonto anintegerratio.
- 38. Betadistribution Beta(5, 10) Beta(10, 5) Beta(50, 50) E(Beta) = # actions / # views
- 39. Responsedistribution Weshouldrepresenteachtypeofactionwithitsownbeta distribution,anditsownreward. Wehavealinearcombinationofresponsesfromeachbandit. + … + rhide *rsave * Recs HideSave Friends Recs Friends
- 41. Howdidwedo? Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools. Desiredtraits • Userswithsignificantpriorhistoryshouldseemoreofthecontenttypestheyprefer. • Weshouldbeabletocontroltheblendingrateforuserswithlittlepriorhistory;theseusers shouldseesomeofalltypesofcontent.
- 43. Samplingtechnique • Ineachround,wewillmaptheexpectedvalues ofthepoolutilitiesontoanintegerratio.
- 44. Howdidwedo? Goal:Combinetheresultsfromthepoolsintoonesortedfeed. Constraints • Mustmaintaintherankingofeachpool. • Scoresarenotcomparableacrosspools. Desiredtraits • Userswithsignificantpriorhistoryshouldseemoreofthecontenttypestheyprefer. • Weshouldbeabletocontroltheblendingrateforuserswithlittlepriorhistory;theseusers shouldseesomeofalltypesofcontent.
- 45. Example Rewards Positive action 1 Negative action -1
- 46. Example Prior Values Positive action 50 Negative action 0 View 1000
- 47. Pool # Positive Actions # Negative Actions # Views Utility Function Friends 5000 + 50 0 + 0 10000 + 1000 Recommendation s 0 + 50 500 + 0 10000 + 1000 Userwithhistory
- 48. Pool Utility Function Expected Utility Mapped To Ratio Friends 0.46 10 Recommendations -0.04 0 Userwithhistory
- 49. Userwithhistory
- 50. Newuser Pool # Positive Actions # Negative Actions # Views Utility Function Friends 2 + 50 0 + 0 100 + 1000 Recommendations 2 + 50 0 + 0 10 + 1000
- 51. Pool Utility Function Expected Utility Mapped To Ratio Friends 0.047 5 Recommendations 0.051 5 Newuser
- 52. Newuser
- 53. Howdidwedo?
- 56. Personalizingtheratio .25 FractionPinsseenfromsocialpool Fractionofusers .4 .3 .2 .1 .4 .3 .2 .1 .50 .75 .25 .50 .75 Control Enabled
- 57. Conclusions Thismodelseemssimple,butitworkedincrediblywellinpractice. Whywasitsuccessful? • Tunablevariablesgiveuscontroloverbusinessobjectives. • Easytoaddnewpoolsofcontent. • Didn’trelyonanyinformationfromtherankingmodels.