psychological consumer factors decision making · [1] r. gao et al, improving user profile with...
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PsychologicalFactors
ConsumerDecisionMaking
e.g., Impulsiveness,openness e.g.,Buyingchoices
1. Increase click-through rate predictions2. Enhance recommendation quality3. Improve user experience
Personalization
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PersonalityinComputationalAdvertisingABenchmark
GiorgioRoffo1 &AlessandroVinciarelli2
1UniversityofVerona,IT2 UniversityofGlasgow,UK
4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE)
Vision,ImageProcessing&SoundLab
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OutlineØ IntroductionØ TheADSDataset
• ParticipantData• BigFivePersonalityTraits• Users’Favourite Pictures
Ø PreliminaryResults• ExperimentalSetup• AdClickPrediction• AdRatingPrediction
Ø ConcludingRemarks3
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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.
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TheADSDatasetØ AnewdatasetforAdsRecommendationØ Acollectionof300realadvertisements
Ø Displayformats:• Rich-MediaAds,• ImageAds,• TextAds
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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
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TheADSDataset(2)Ø ADSisfullyannotatedØ 120participantsØ Adsvotedaccordingtoifuserswouldornotclickonthem
Ø Click/NoClickLabels§ Clicked =ratinggreaterorequalto4§ No-Clicked =ratinglessthan4
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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
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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
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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
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BigFivePersonalityTraitsØ ADSisenrichedwithusers’psychologicalfactors[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
[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)
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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
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Users’Favourite PicturesØ ADScontainsmorethan1,200personalpictures• (labelledaspositive/negative)
Ø SentimentAnalysis• Inferringpersonalityfromfavouriteusers’pictures[1]• Incorporatingaffective-likefeaturescomingfromusers’favouritepictures[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 Positive
Negative
[1]PietroLovatoetAl,Faved!Biometrics:TellMeWhichImageYouLikeandI'llTellYouWhoYouAre,TransactionsonInformationForensics 2014[2]C.Segalin etAl,ThePicturesweLikeareourImage:ContinuousMappingofFavorite PicturesintoSelf-AssessedandAttributedPersonalityTraits,TransactionsonAffectiveComputing,2016
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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, …
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ExperimentalSetup(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
Ø Experimentswereperformedat“CategoryLevel”
Ø Balancedclickdistributionoverthe20categories• 1,229clickedinstances• 1,171notclickedinstances
𝒙𝒊 = 𝒇𝟏, 𝒇𝟐, 𝒇𝟑, …
𝒚𝒊 =𝒙𝒊𝒘𝒊
𝒇𝑩𝟓Click/No-Click
Ratings1-5 Regression
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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
…
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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
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%
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AdRatingPrediction
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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: 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
…
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AdRatingPrediction
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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 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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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
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Thankyouforyourattention
Vision,ImageProcessing&SoundLab
v Downloadpage: http://giorgioroffo.it/datasets/ADS16-main-download.zipPassword:EMPIRE2016Size:751MB
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Within-SubjectUserStudy
Figure 2. Spider-Diagrams for O-C-E-A-N Big-Five traits
Ø Clustering the Big-Five traits by Affinity Propagation
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Extra Slides
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Within-SubjectUserStudy(2)
Supportstechnologicalinnovation
Strongwillingnesstobuyproductsonline
Attitudetowardtransformationalads 22
Extra Slides
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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
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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
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Evaluation Methodology
(RootMeanSquareError)
(MeanSquareError)
(MeanAbsoluteError)
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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