PsychologicalFactors

ConsumerDecisionMaking

e.g., Impulsiveness,openness e.g.,Buyingchoices

1. Increase click-through rate predictions2. Enhance recommendation quality3. Improve user experience

Personalization

PersonalityinComputationalAdvertisingABenchmark

GiorgioRoffo1 &AlessandroVinciarelli2

1UniversityofVerona,IT2 UniversityofGlasgow,UK

4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE)

Vision,ImageProcessing&SoundLab

OutlineØ IntroductionØ TheADSDataset

• ParticipantData• BigFivePersonalityTraits• Users’Favourite Pictures

Ø PreliminaryResults• ExperimentalSetup• AdClickPrediction• AdRatingPrediction

Ø ConcludingRemarks3

Introduction

[3]C.A.Turkyilmaz etAl.,Theeffectofpersonalitytraitsandwebsitequalityononlineimpulse buying,InConf.ICSIM 2015.[2]M.Tkalcic andLiChen,PersonalityandRecommenderSystems,InRecommenderSystemsHandbook,2015.[1]R.GaoetAl,Improvinguserprofilewithpersonalitytraitspredictedfromsocialmediacontent,InConf.RecSys,2013.

Ø Shoppingonlineplaysanincreasinglysignificantroleinourlives

Ø Personalityinfluencespeople’sbehaviorandinterests[3,4]

Ø Personalityisincreasinglyattractingattention• UserProfiling[1]• RecommenderSystems[2]

Identifyaneed

CollectInformation

EvaluateAlternatives

Makeapurchasedecision

Consumers’decisionmakingprocess CollectInformation

EvaluateAlternatives

Identifyaneed

4

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

[4]MLauriola etAl.,Personalitytraitsandriskydecision-makinginacontrolledexperimentaltask:Anexploratorystudy,InPersonalityandIndividualDifferences,2001.

TheADSDatasetØ AnewdatasetforAdsRecommendationØ Acollectionof300realadvertisements

Ø Displayformats:• Rich-MediaAds,• ImageAds,• TextAds

5

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

TheADSDataset(2)Ø ADSisfullyannotatedØ 120participantsØ Adsvotedaccordingtoifuserswouldornotclickonthem

Ø Click/NoClickLabels§ Clicked =ratinggreaterorequalto4§ No-Clicked =ratinglessthan4

6

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

TheADSDataset(3)Ø Adsbelongto20product/servicecategories

Ø 15Adsforeachcategory7

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

ParticipantdataØ UserPreferences(fromapredefinedlistofcategories)

Ø MostvisitedwebsitesØ MostwatchedfilmsØ MostlistenedmusicØ …Ø FavouritesportsØ HobbiesØ Traveldestinations

Ø Demographicinformation(fromapredefinedlistofchoices)Ø Age,gender,nationality,home-townØ job,typeofjob,monetarywell-being

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Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

BigFivePersonalityTraitsØ ADSisenrichedwithusers’psychologicalfactors[1]

9

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

[1]B.Rammstedt,“Measuringpersonalityinoneminuteorless:A10-itemshortversionoftheBigFiveInventoryinEnglishandGerman”JournalofResearchinPersonality,2007.

Creative,Curious

Conservative,Traditional

HIGH LOW

Opennesstoexperience(O)

HighResponsible,Organized

Unreliable,Easy-goingConscientiousness(C)

Assertive,Talkative

Timid,ReservedExtroversion(E)

Co-operative,Trusting

Cold,AntagonistAgreeableness(A)

Nervous,Insecure

Calm,SecureNeuroticism(N)

BigFivePersonalityTraits(2)Ø TheeffectofB5traitsonattitudestowardadvertisements[2]

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Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

[2]D.Myersetal,Themoderatingeffectofpersonalitytraitsonattitudestowardadvertisements:acontingencyframework,Management&MarketingChallengesforKnowledgeSociety,2010

Creative,Curious

HIGH

Opennesstoexperience(O)

HighResponsible,OrganizedConscientiousness(C)

Assertive,TalkativeExtroversion(E)

Co-operative,TrustingAgreeableness(A)

Nervous,InsecureNeuroticism(N)

Supportstechnologicalinnovation

Informationgatheringand

detailedprocessing

Attitudetowardtransformationalads

Attitudefornon-comparativeads

Attitudeforcomparativeads

ADS

Users’Favourite PicturesØ ADScontainsmorethan1,200personalpictures• (labelledaspositive/negative)

Ø SentimentAnalysis• Inferringpersonalityfromfavouriteusers’pictures[1]• Incorporatingaffective-likefeaturescomingfromusers’favouritepictures[2]

11

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks Positive

Negative

[1]PietroLovatoetAl,Faved!Biometrics:TellMeWhichImageYouLikeandI'llTellYouWhoYouAre,TransactionsonInformationForensics 2014[2]C.Segalin etAl,ThePicturesweLikeareourImage:ContinuousMappingofFavorite PicturesintoSelf-AssessedandAttributedPersonalityTraits,TransactionsonAffectiveComputing,2016

ExperimentalSetup(1)

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Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

Ø X setofobservations𝐗 = 𝒙𝟏𝒙𝟐 …𝒙𝑵

Ø N =120subjectsØ 𝒙𝒊 feature vector for subject iØ 1-of-K representation

𝒇𝟏 = 𝑭𝒂𝒗𝒐𝒓𝒊𝒕𝒆𝒘𝒆𝒃𝒔𝒊𝒕𝒆𝒔 , 𝑓8∈ [0,1]𝒇𝟐 = 𝑭𝒂𝒗𝒐𝒓𝒊𝒕𝒆𝒇𝒊𝒍𝒎𝒔 , 𝑓@ ∈ [0,1]𝒇𝟑 = 𝑭𝒂𝒗𝒐𝒓𝒊𝒕𝒆𝒎𝒖𝒔𝒊𝒄 , 𝑓D ∈ [0,1]…

𝒙𝒊 = 𝒇𝟏, 𝒇𝟐, 𝒇𝟑, …

ExperimentalSetup(2)

13

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

Ø Experimentswereperformedat“CategoryLevel”

Ø Balancedclickdistributionoverthe20categories• 1,229clickedinstances• 1,171notclickedinstances

𝒙𝒊 = 𝒇𝟏, 𝒇𝟐, 𝒇𝟑, …

𝒚𝒊 =𝒙𝒊𝒘𝒊

𝒇𝑩𝟓Click/No-Click

Ratings1-5 Regression

AdClickPrediction

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Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

Ø Task: Recommend advert on which a user most probably clicks

Ø 120 users: 12 folds of 10 users eachØTraining on 11 folds (110 users)ØTesting on 1 (10 users)

1 0 0 0 1 0 1 1 0 1 0 0 1 1 1 0 1 0 1 1

0 1 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1 0 1 1

𝑈I8

𝑈I88J

0 1 0 1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 1 0

0 0 1 1 1 1 1 0 0 1 0 1 0 0 0 0 1 1 0 1

𝑈I888𝑈I8@J

10-foldcross-validation

Training

Testing

…

AdClickPrediction

15

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

ØAccuracy Measure:Ø Area Under the ROC Curve (AUC)

ØLogistic Regression (without-Big5 features): 51.9%

ØLogistic Regression (+Big5 features): 53.4%

tpr

fpr

AUC ROC-Curve

+1.5%

AdRatingPrediction

16

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

Ø Task: Predict the feedback a user would give to an advert (from 1 star to 5 stars)

2.5 3 2 2.3 3.5 2.4 5 4.5 4.2 3.3 2.5 4.6 2.1 3.8 3.1 2.1 1.1 2.3 1.1 1.1

3.5 3.6 2.2 2.4 4.5 5.6 3.2 4.2 2.9 4.8 3.8 2.7 4.9 5 3.3 2.8 1.5 2.8 3.2 1.9

𝑈I8

𝑈I88J

2.5 3 3.8 3.1 2.1 1.1 2.3 3.6 2.2 2.4 4.5 5.6 3.6 4.2 2.3 3.5 2.4 2.3 3.5 2.4

5 4.5 4.2 3.3 2.5 4.6 2.1 4.2 2.9 4.8 3.8 2.7 4.9 4.5 5.6 3.2 4.2 4.5 3.2 1.9

𝑈I888𝑈I8@J

10-foldcross-validation

Training

Testing

…

AdRatingPrediction

17

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

Ø Accuracy Measure: Root Mean Square Error

Ø Logistic Regression (without-Big5 features): 0.20Ø Logistic Regression (+Big5 features): 0.16Ø T-test (p-value < 0.01, Lilliefors Test H=0)

RMSE= 8|L|∑ (�̂�Q,R − 𝑟Q,R)@�(Q,R)∈L

�

2.5 3 3.8 3.1 2.1 1.1 2.3 3.6 2.2 2.4 4.5 5.6 3.6 4.2 2.3 3.5 2.4 2.3 3.5 2.4

5 4.5 4.2 3.3 2.5 4.6 2.1 4.2 2.9 4.8 3.8 2.7 4.9 4.5 5.6 3.2 4.2 4.5 3.2 1.9

𝑢I888𝑢I8@J

R2.5 3 3.8 3.1 2.1 1.1 2.3 3.6 2.2 2.4 4.5 5.6 3.6 4.2 2.3 3.5 2.4 2.3 3.5 2.4

5 4.5 4.2 3.3 2.5 4.6 2.1 4.2 2.9 4.8 3.8 2.7 4.9 4.5 5.6 3.2 4.2 4.5 3.2 1.9

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𝑹X

�̂�Q,8

𝑟Q,8 𝑟Q,@J

�̂�Q,@J

ConcludingRemarks

Ø AnewdatasetforAdsRecommendationØ Acollectionof300realadvertisementsØ 120unacquaintedusersØ Users’PreferencesØ DemographicinformationØ Personalitytraits(Big-FiveFactorModel)Ø UsersFavouritePictures(traitsinference)

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vDownloadpage: http://giorgioroffo.it/datasets/ADS16-main-download.zipPassword:EMPIRE2016Size:751MB

Introduction

The ADS Dataset§ Participant data§ Big Five Personality

Traits§ Users’ Favourite

Pictures

Preliminary Results§ Experimental Setup§ Ad Click Prediction§ Ad Rating Prediction

Concluding Remarks

Thankyouforyourattention

Vision,ImageProcessing&SoundLab

v Downloadpage: http://giorgioroffo.it/datasets/ADS16-main-download.zipPassword:EMPIRE2016Size:751MB

20

Within-SubjectUserStudy

Figure 2. Spider-Diagrams for O-C-E-A-N Big-Five traits

Ø Clustering the Big-Five traits by Affinity Propagation

21

Extra Slides

Within-SubjectUserStudy(2)

Supportstechnologicalinnovation

Strongwillingnesstobuyproductsonline

Attitudetowardtransformationalads 22

Extra Slides

EvaluationMethodology

Ø AdclickPrediction:Predictadvertsonwhichausermostprobablyclick

• Precision = YZYZ[\Z

• Recall (TruePositiveRate)= YZYZ[\]

• FalsePositiveRate= \Z\Z[Y]

• AreaUndertheROC Curve(AUC)

Recommended Notrecommended

Clicked True-Positive(tp) False-Negative(fn)

Notclicked False-Positive(fp) True-Negative(tn)

tpr

fpr

AUCROC-Curve

Evaluation Methodology

23

EvaluationMethodology

Ø AdRatingPrediction:Predictthefeedbackauserwouldgivetoanadvert(from1starto5stars)

• RMSE= 8|L|∑ (�̂�Q,R − 𝑟Q,R)@�(Q,R)∈L

�

• MSE= 8|L| ∑ (�̂�Q,R − 𝑟Q,R)@�(Q,R)∈L

• MAE= 8|L|∑ |�̂�Q,R − 𝑟Q,R|�(Q,R)∈L

�

Evaluation Methodology

(RootMeanSquareError)

(MeanSquareError)

(MeanAbsoluteError)

24

AdClickPredictionExperiments and Results§ Ad Click Prediction

Table 5: Performance for ad click prediction (F-Measure). Big-Five features systematically contribute to the overall performance.

Ø Results show the effect of B5 on Ads recommendation

Ø Fraction of recommended Ads that are clicked (Precision)Ø Fraction of clicked Ads that are recommended (Recall)

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AdRatingPredictionExperiments and Results§ Ad Rating Prediction

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AdRatingPrediction(2)

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Extra Slides