Personalized Model for Temporal Human Behavior Predic&on · • Lag of order i at &me y t: y t-i...

MAPer:AMul&-scaleAdap&vePersonalizedModelforTemporalHumanBehaviorPredic&on

SarahMasudPreumJohnA.Stankovic

YanjunQi

•  Genera&onofhumanbehaviordata–  Onlinebehavior:

•  socialmedia,searchlog•  Targetedadver&sing/contentsharing•  PersonalizedIR

–  Offlinebehavior:•  Sensors,smartdevices•  Predic)ngoccupancyandenergyusage•  Anomalydetec&oninassistedlivingfacili&es

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ModelingTemporalHumanBehavior

ChallengesMo&va&onRelatedWorksApproach Experiments ConclusionResultsMo&va&on

•  Temporalsmoothness(lag)–  Workingfrom3pmto4pm

(andthencon&nueaWer4pm)

•  Behaviorrhythm(cycle)–  WatchingTVateverySaturdaynight

•  Interac&onamongmul&pleac&vi&es–  Working&lllatenightdelayssleep&me

3

FactorsAffec&ngRegularTemporalBehavior

ChallengesMo&va&onRelatedWorksApproach Experiments ConclusionResultsChallenges

Factorsvaryacrossindividualsandbehavior

Personalizedmodels

– Factorsvaryovermul&-scaletemporalcontexts•  Houroftheday•  Dayoftheweek

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DynamicNatureOfBehavior

Adap&vemodeling

ChallengesMo&va&onRelatedWorksApproach Experiments ConclusionResultsChallenges

•  Extractsfeaturesfrommajortemporalfactors•  Encodesmul&-scaletemporalcontextstoensureadap&velearning

•  Alinearpredic&vemodelwithexplanatorypower

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Contribu&on:Mul&-scaleAdap&vePersonalizedModel(MAPer)

ChallengesMo&va&onRelatedWorksApproach Experiments ConclusionResultsApproach

Solu&onOverview

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Behavior&meseries

BehaviorMatrix

Extractfeatures:lag,cycle,interac)on,temporalcontext

Usefeaturesforpredic&on

Crea&ngBehaviorTimeSeries•  Quan&fybehaviorinthetemporaldomainasdiscrete

behaviorsample

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0

0.5

1

1.5

2

2.5

3

3.5

11/1/2009 12/1/2009 1/1/2010 2/1/2010 3/1/2010

Num

bero

ftweets/ho

ur

TwiGerbehavior)meseriesfor15thhour

Crea&ngBehaviorSampleMatrix

8

…

H1 H5H3 H24H16H7 H14

SatSunMonTueWedThuFri

…

………

…

… Behavioral

Rhythm

TemporalSmoothness

…

…

………

…

…

BehaviorSampleMatrix

Countoftweet


Lowàhigh

Sat

LagandCycleFeatures

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•  Lagoforderiat&meyt:yt-i

•  Cycleofbehavior&meseries

∑ ⋅+∑ ⋅=

−=

−≈c

jcjtj

l

iitit yyy

1)(

1)(ˆ βα

∑ ⋅=

−≈l

iitit yy

1)(ˆ α


TemporalContextFeatures

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•  Dailybasisvector:

•  Weeklybasisvector:

10

…H1 H5H3 H24H22H7 H20H18

Sat

00100000……0000000

Example:Basisvectorforhour3


dB!

wB!

Example:BasisvectorforSaturdayD1 D5D3

1000000

D7

Quan&fytheeffectoftemporalcontextonbehavior

FeaturesforaSingleAc&vity

1111

),(ˆˆˆ11

wdk

C

j

kj

L

i

ki

k k

jct

k k

it

k

t yyy ΒΒ⋅+∑ ⋅+∑ ⋅≈==

−−

!!γβαLag Cycle TemporalContext

Onlydailybasisvector:DailyscaleAdap&vePersonalizedmodel(DAPer)


Onlyweeklybasisvector:WeeklyscaleAdap&vePersonalizedmodel(WAPer)

Bothand:Mul&-scaleAdap&vePersonalizedmodel(MAPer)

dB!

wB!

dB!

wB!

Interac&onFeaturesforMul&pleAc&vi&es


BehaviormatrixforAc&vityk

BehaviormatrixforAc&vityk’1


klt

Sk

L

l

kkl

wdk

C

j

kct

kj

i

kit

ki

kt

yW

y

yy

k

k

j

kL

′′−

∈′ =′

′′

=−

=−

⋅+

ΒΒ⋅+

∑ ⋅+

∑ ⋅≈

∑∑′

1

),(

1

1

),(

ˆ

!!γβ

α

interac)on

Interac&onFeaturesforMul&pleAc&vi&es


BehaviormatrixforAc&vityk



ac)vity Interac)onsR1sleep Brushingteeth

UsinginternetWatchingTV

R2sleep BrushingteethWatchingTVWashingdishes

R1snack WatchingTVUsinginternetTalkingonphone

… …

interac)on

•  Timeseriespredic&on–  SeasonalARIMA(SARIMA)model–  Doesnotconsidertemporalcontextofbehavior

•  Modelingonlineuserbehavior–  Searchquery[K.Radniskyetal,WWW2012;J.Yangetal,WSDM2011]–  Socialmediaposts[F.Abeletal,UMAP2011]–  Focusontemporalpanernofusergeneratedcontentsratherthan

actualuserbehavior

•  Modelingofflineuserbehavior–  ComputervisionandWSN:Sensingandrecognizingdifferentac&vi&es

ofdailyliving–  Don’tfocusonpredic&ng

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RelatedWorks

ChallengesMo&va&onRelatedWorksApproach Experiments ConclusionResultsRelatedWorks

OverviewofEvalua&on•  Predic&ngbehaviorintensityata&meintervalasaregressionproblem–  Performancemetric:MSE,PearsonCorrela&on

•  4realdatasets•  Comparisonwith–  Parametricandnonparametricbaselines–  state-of-artSARIMAmodel

•  Sensi&vityanalysis

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ChallengesMo&va&onRelatedWorksApproach Experiments ConclusionResultsExperiments

Datasets

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Dataset Span #ofusers BehaviorsampleTwiner 5months 1274 #oftweets/hour

Searchlog

3months 1307 #ofuniquesearchqueries/hour

OnlineBehaviorData

Dataset Span #ofresident BehaviorsampleARAS 1month 2

Dura&onofac&vity/halfhourHOLMES

3months 1

OfflineBehaviorData

Baselines•  Movingaverageoverbothlagandcycleterms(MA)

•  Auto-regressivemethodwithcyclefeature(AR-C)

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∑ ⋅+∑ ⋅=

−=

−≈c

jctj

l

iitit jyyy

1(

1)( )ˆ βα


⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛+=∑∑ = −= −

c

y

ly

yc

j jtl

i itt

1 )24*(121ˆ

ComparingwithBaselines•  Onaverage,MAPerreducesMSEby10%andincreasesPearson

correla&onby83%

18

ChallengesMo&va&onRelatedWorksApproach Experiments ConclusionResultsResults

0

0.2

0.4

0.6

0.8

1

Twiner Searchlog

ARAS HOLMES

MeanSquareError PearsonCorrela)on

0

0.01

0.02

0.03

0.04

0.05

0.06

TwinerSearchlogARAS HOLMES

MA

AR-C

MAPer

ComparingwithStateofArt•  Onaverage,MAPerreducesMSEby14%andincreasesPearson

Correla&onby44%thanSARIMA

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00.010.020.030.040.050.060.07 SARIMA

MAPer

0

0.2

0.4

0.6

0.8

1

Twiner Searchlog

ARAS HOLMES

MeanSquareError PearsonCorrela)on

EffectofTemporalContextFeatures•  Dailycontextismoreusefulthanweeklycontext

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EffectofInterac&onFeatures•  Interac&onfeaturesimprovestheperformance

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Results:ExplanatoryPowerofMAPer

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•  Quan&fyeffectofdifferentfeatures•  Detec&ngusersimilaritymoreprecisely

InsightsfromresultsExperiment OnlineBehavior OfflineBehavior

Predic&on MAPer MAPerwithintera&on

Personaliza&on ü NA

Adap&veLearning ü ü

Varia&onoftemporalwindowlength Higherbener Variesforeachac&vity:

havingsnackvssleep

Varia&onoftrainingsetsize Lowerbener

Varia&onoflag Nosignificanteffect

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ConcludingRemarks•  Apersonalizedinterpretablemodelfortemporalbehavior

predic&on•  Virtualandphysicalbehavior•  Someregularityinbehaviorthatconformswithhoursofthe

day,dayoftheweek

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ChallengesMo&va&onRelatedWorks Approach Experiments ConclusionResults Conclusion

Thanks!

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Personalized Model for Temporal Human Behavior Predic&on · • Lag of order i at &me y t: y t-i...

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