modeling users for online advertising

115 Pages·2016·6.38 MB·English

MODELING USERS FOR ONLINE ADVERTISING
BYQIANGMA
Adissertationsubmittedtothe
GraduateSchool—NewBrunswick
Rutgers,TheStateUniversityofNewJersey
inpartialfulfillmentoftherequirements
forthedegreeof
DoctorofPhilosophy
GraduatePrograminComputerScience
Writtenunderthedirectionof
S.Muthukrishnan
andapprovedby
NewBrunswick,NewJersey
October,2016
ABSTRACTOFTHEDISSERTATION
Modeling Users for Online Advertising
byQiangMa
DissertationDirector: S.Muthukrishnan
Onlineadvertisingisabletotargetusersatafinelevelofgranularity. Todothiseffectively,
models are required to represent users and their behavior. In this thesis, we studied several
problemsrelatedtomodelsofonlineusers. Inonlineadvertising,advertisersandadplatforms
use user profiles as the language to target users, composed of user information from demo-
graphics,location,andinterests. Weimplementedauser-profile-drivenadcrawlingframework
and empirically investigated the relationship between user profiles and the ads to which they
are exposed. We observed user profiles to play a greater role in display ads than in video ads.
Furthermore,themainmodeofaccessingonlinecontenthasbeenshiftingfromwebsitebrows-
ing to mobile application usage. Mobile apps have become the building blocks to model user
behavior on mobile devices. We designed a neural network model (app2vec) to vectorize
mobile apps by studying how users employ these apps. We analyzed the learned app vectors
qualitativelyandquantitativelyandusedthemtoextractuserappusageprofilesforapp-install
advertising. Finally,advertisersarefacedwiththechallengeoffindingtheoptimaluserprofile
properties to target. We designed a look-alike audience extension system, where advertisers
providealistofpastconvertersas”seedusers”andoursystemdeterminesuserssimilartothe
seed. Ratherthanassuminglinearseparabilityoflookalikeandnon-lookalikeusers,asinprior
work,weproposeanewapproachwithnearest-neighborfiltering. Oursystemworksefficiently
forbillionsofusersandimprovestheadcampaignconversionrateinpracticeatYahoo!.
ii
Acknowledgements
This thesis could never have been completed without the hard work and support of several
people. I am forever grateful to my advisor Prof. S. Muthukrishnan. Through our numer-
ous conversations and joint projects, I was fortunate enough to be influenced by his system
of questioning, reasoning and presenting. In addition to providing academic instruction, Prof.
Muthukrishnanalsohelpedmetobecomeamorepositiveandhealthyperson. Hisactivesup-
portplayedanessentialroleinensuringthesurvivalofmeandmyfamilyinournewhome.
I would like to thank my committee members–Prof. Muthukrishnan, Prof. Nath, Prof.
Imielinski and Dr. Kale–for taking the time to serve on my committee and for providing me
withvaluablefeedbackonmythesiswork.
Manythankstomyformeradvisor,Prof. DanfengYao,foryourguidanceatthestartofmy
graduatestudyandforintroducingmetotheresearchworld. Mysincerethankstomyadvisors
inmymaster program: Prof. FrankHsu, Prof. GaryWeissandProf. DamianLyons. Without
yourguidanceandassistancewithmyresearchwork,Iwouldneverhavehadanopportunityto
getintoaPhDprogram.
Alongmyjourney, Ihavebeenmorethanfortunatetohavemanysupportivementorswho
have helped me see beyond the ivory tower. My first internship mentor at MLP, Michael Kur-
tis, benefited me with his professionalism and his sharp mind. More importantly, he gave me
the courage to pursue a PhD course. My research internship mentor at Narus, Han Hee Song,
trained me for the R&D work environment. I would also like to thank my research intern-
ship mentor at Flurry, Wil Simpson, whose elegant way of breaking down complex practical
problemsandwhoseorganizedworkingstylehavegreatlyinfluencedmyapproachtowork.
MyfondestmemoriesofgraduatelifeareofworkingwithmyfriendsattheMassDALlab,
includingDarjaKrushevskaja,PriyaGovindan,VasishtGopalan,EdanHarel,BrianThompson,
andJohnRobertYaros. Thankyoufortheskillsyoutaughtmeandforthecourageandsupport
iii
yougavemeduringhardtimes. IamgratefultohaveworkedcloselywithDarjaKrushevskaja
on many projects. She has always been an honest friend and helped me to overcome several
difficulties.
Iwouldnothavebeenabletomakesomuchprogresswithoutclosecollaborationwithmy
talented and diligent colleagues at Yahoo!. My sincere thanks go out to Datong Chen, Zhen
Xia, Musen Wen, Peiji Chen, Liang Wang, Jialing Liu, Jiayi Wen, Eeshan Wagh, and Robert
Ormandi. I am indebted to Stratis Ioannidis for helping me to start breathing industrial scale
big data. It has been my great honor to work with my co-authors and collaborators: Graham
Cormode, Brian Thompson, Paul Barford, Igor Canadi, Mark Sandler, Ye Tian, Liang Wang,
KuiXu,DanfengYao,AlexanderCrowell,SwatiRallapalli,MarioBaldi,LiliQiu,andAntonio
Nucci.
Icanneverthankmyfamilyenoughfortheircontinuedlove,support,patienceandforgive-
ness. My father and mother have given everything they had to support my growth, including
bravery to explore the world. In addition, my in-laws have always been supportive and I am
verygrateful.
Without the love and understanding of my dear loving wife, Qing Zhang, I would not be
where I am today. She has given me the strength and encouragement necessary to face the
world. Iwilltrytogethomeearlyfromwork,mylove.
iv
Dedication
Tomyparents,mywifeandmyson.
v
Table of Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
ListofTables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
ListofFigures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2. OverviewofUserOnlineAdTargeting . . . . . . . . . . . . . . . . . . . . . . 5
2.1. OnlineAdvertising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2. UserTargeting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3. ResearchDirections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3. ObserveUserOnlineAdvertisingProfileandAdTargeting . . . . . . . . . . . 13
3.1. OnlineAdLandscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.1.1. TheAnatomyofOnlineAdvertising . . . . . . . . . . . . . . . . . . . 14
3.1.2. AdFormats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2. MeasurementMethodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.1. SyntheticUserProfiles . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.2. ControlledWebpageCrawling . . . . . . . . . . . . . . . . . . . . . . 21
3.2.3. ControlledVideoWatching . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3. ExperimentsandAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3.1. DisplayAdTargeting . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3.2. VideoAdTargeting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
vi
3.3.3. PolymorphicVideosandUserInterests . . . . . . . . . . . . . . . . . 38
3.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5. Relatedwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4. UserModelingonMobile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.1. TheAppSimilarityProblem . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2. MobileAppModeling–app2vec . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.1. word2vecBackground . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.2. AppVectorization–app2vec . . . . . . . . . . . . . . . . . . . . . 53
4.3. app2vecinDiverseAdConsiderationSetSelection . . . . . . . . . . . . . . 60
4.3.1. DeterminantalPointProcess(DPP)Background . . . . . . . . . . . . 60
4.3.2. OurApproach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3.3. ExperimentsandResults . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.4. app2vecinAppRecommendation . . . . . . . . . . . . . . . . . . . . . . . 66
4.4.1. DPP-basedAppClustering . . . . . . . . . . . . . . . . . . . . . . . . 66
4.4.2. ExperimentsandResults . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.5. app2vecinApp-installAdConversionPrediction . . . . . . . . . . . . . . . 69
4.6. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.7. Relatedwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5. LargeScaleLook-alikeAudienceModeling . . . . . . . . . . . . . . . . . . . . 74
5.1. Look-alikeAudienceTargeting . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2. ExistingLook-alikeMethods&Systems . . . . . . . . . . . . . . . . . . . . . 75
5.2.1. SimpleSimilarity-basedLook-alikeSystem . . . . . . . . . . . . . . . 76
5.2.2. Regression-basedLook-alikeSystem . . . . . . . . . . . . . . . . . . 77
5.2.3. SegmentApproximation-basedLook-alikeSystem . . . . . . . . . . . 78
5.3. Graph-ConstraintLook-alike . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.3.1. PhaseI:GlobalGraphConstruction . . . . . . . . . . . . . . . . . . . 80
vii
5.3.2. PhaseII:CampaignSpecificModeling. . . . . . . . . . . . . . . . . . 83
5.3.3. SystemDesignandPipeline . . . . . . . . . . . . . . . . . . . . . . . 85
5.4. ExperimentsandResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.6. RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6. FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
viii
List of Tables
3.1. Ratiooftargetedadsforexamplewebsites. . . . . . . . . . . . . . . . . . . . 27
3.2. SamplecategorymappingsforAlexaandWebPulse. . . . . . . . . . . . . . . 30
4.1. Exampleoftopsimilarappwithdatapreprocessing. . . . . . . . . . . . . . . . 55
4.2. Examplesofrelevantappsfromapp2vecmodel. . . . . . . . . . . . . . . . . 56
4.3. Appsimilarityquantitativeevaluationcriteria. . . . . . . . . . . . . . . . . . . 57
4.4. Appsimilarityquantitativeevaluations. . . . . . . . . . . . . . . . . . . . . . 57
4.5. Examplesofappanalogies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.6. Apprecommendationperformancecomparison. . . . . . . . . . . . . . . . . . 68
ix
List of Figures
2.1. TrackersonNewYorkTimeshomepage. . . . . . . . . . . . . . . . . . . . . . 8
2.2. TrackersonasciencearticlepagefromtheNewYorkTimes. . . . . . . . . . . 8
2.3. UserprofileexamplefromGoogle. . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4. Interactionsamonguserprofile,contentandads.. . . . . . . . . . . . . . . . . 10
3.1. Publishers’commonadtypes. . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2. Adformatexamples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3. ExampleofdifferentadformatsonYouTube. . . . . . . . . . . . . . . . . . . 17
3.4. Dynamicsofthenumberofprofileinterestsaswebsitesarevisited. . . . . . . . 19
3.5. AdCrawlerComponents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.6. Averagetrafficrankofadvertisers. . . . . . . . . . . . . . . . . . . . . . . . . 26
3.7. Examplesofadcreativeswithdifferenttargetingstrategies. . . . . . . . . . . . 29
3.8. Distributionofimpressionsovercategories. . . . . . . . . . . . . . . . . . . . 31
3.9. Collectedadamountdistributionbyprofile. . . . . . . . . . . . . . . . . . . . 32
3.10. Videoadsdistributionbytoplevelcategories. . . . . . . . . . . . . . . . . . . 33
3.11. Interestsaddedbymarkedactivity. . . . . . . . . . . . . . . . . . . . . . . . . 33
3.12. Interestsremoved. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.13. Addedintereststhatareexplainedbyvideoverticals. . . . . . . . . . . . . . . 35
3.14. ImpactofYouTubeprerolladstotheprofile. . . . . . . . . . . . . . . . . . . . 36
3.15. ImpactofYouTubesponsoredadstotheprofile. . . . . . . . . . . . . . . . . . 37
3.16. ImpactofYouTubeadstotheprofile.. . . . . . . . . . . . . . . . . . . . . . . 38
3.17. Similaritiesofprofilesforintraandinterpolymorphicvideosets. . . . . . . . . 41
3.18. Profilesimilaritycomparisonfor15setsofpolymorphicvideos. . . . . . . . . 42
4.1. Popularityofappcategories. . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2. Performancecomparisononadclickpredictionsforsampledusers. . . . . . . . 65
x