adversarial substructured representation learning for
TRANSCRIPT
Presenter: PengyangWangPengyang Wang, Yanjie Fu, Xiaolin Li, Hui Xiong
Adversarial Substructured Representation Learning for Mobile User Profiling
1
Outline
¨ BackgroundandMotivation¨ Definition and ProblemStatement¨ Methodology¨ Evaluation¨ Conclusion
Motivation Application: Toward Adaptive User Interfaces2
Mobileuserprofiling
Asimilaritygraphofusers,transportations,ODpairs
Adaptiveinterfacesby:(1) inferringtrippurposes,(2) transportmodes,(3) origin-destinationpairstoimproveuserengagementandperformances
Challenge I: Implicit User Patterns inMobile Activities3
¨ Humanactivities arespatially,temporally,andsociallystructural.
• Howcanweidentifyadatastructuretobetterdescribeamobileuser’sactivities?
From Users To Activity Graphs4
Spatial-temporaltransitionpatterns
UserActivityGraph
Office Hospital
Costco
Gas Station
MacDonaldWalmart
PreSchool
Auto Service
Home
Zoo
Sixflag
PizzaHut
RegalCinemas
Lab
Library IT
Department
Problem Formulation: Representation Learning with Activity Graphs5
f(,)=z
• Givenauserandcorrespondinguseractivitygraph,weaimtomaptheusertoaprofilevector
Office Hospital
Costco
Gas Station
MacDonaldWalmart
PreSchool
Auto Service
Home
Zoo
Sixflag
PizzaHut
RegalCinemas
Lab
Library IT
Department
UserProfileVector
Global Behavioral Pattern
¨ Entire structures: howauser’sactivitiesgloballyinteractwitheachother(stronglylink,weaklylink,nolink)
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Office Hospital
Costco
Gas Station
MacDonaldWalmart
PreSchool
Auto Service
Home
Zoo
Sixflag
PizzaHut
RegalCinemas
Lab
Library IT
Department
Substructure Behavioral Pattern7
Office Hospital
Costco
Gas Station
MacDonaldWalmart
PreSchool
Auto Service
Home
Zoo
Sixflag
PizzaHut
RegalCinemas
Lab
Library IT
Department
¨ Substructures:topologyofsubgraphsthatfeaturetheuniquebehavioralpatternsofauser’sactivities
A Young Faculty with Young Kids
Circle:Personalized
Preference For A Close-loop
Consecutive Activity Pattern
High-frequencyVertexes: Personalized
Interests For ASpecific Type of
Activities
f(,, )=
Problem Reformulation: Representation Learning with Global and Sub-Structure Awareness8
Office
Lab
Library IT
Department
Office
PreSchool Home
UserProfileVector
Entire Structure Patterns SubstructurePatterns
Office Hospital
Costco
Gas Station
MacDonaldWalmart
PreSchool
Auto Service
Home
Zoo
Sixflag
PizzaHut
RegalCinemas
Lab
Library IT
Department
Approximating Substructure Detector14
Encoder Decoder
SubstructureDetector
Discriminator+
-Real
Fake
1
0
Real
Fake
Deep First Search
Substructure
Substructure
xxDeep First
Search Substructure (Label)
ss
Training CNN
Pre-train aConvolutionalNeuralNetwork(CNN)toapproximatethetraditionalsubstructuredetector
Not differentiable
Approximating Substructure Detector14
Encoder Decoder
SubstructureDetector
Discriminator+
-Real
Fake
1
0
Real
Fake
Deep First Search
Substructure
Substructure
Not differentiable
xxDeep First
Search Substructure (Label)
ss
Training CNN
Pre-train aConvolutionalNeuralNetwork(CNN)toapproximatethetraditionalsubstructuredetector
Summary15
Encoder Decoder
Pre-trained CNN
xx xx
Substructure
ss
Substructure
ss
Discriminator+
-RealFake
1
0
Real
Fake
zz
• GeneratorAutoencoder linked with anapproximated substructuredetector (pre-trained CNN)
• DiscriminatorAmultilayerpercetron
• AdversarialTraining• Discriminatoraccuracy
• Generatorloss
Optimization16
¨ Training
¨ Testing
Discriminator Loss Generator Loss
Reconstruction Loss
Encoderxx
zz
Well Trained
What To Do Next: Inferring Next Activity Type17
User Representation
Next Activity Recommendation
User Profiling
User
Adversarial Substructured Learning
……
Office Hospital
Costco
Gas Station
MacDonaldWalmart
PreSchool
Auto Service
Home
Zoo
Sixflag
PizzaHut
RegalCinemas
Lab
Library IT
Department
Encoder Decoder
SubstructureDetector
Discriminator+
-Real
Fake
1
0
Real
Fake
SubstructureDetector
Substructure
Substructure
User Activity Graph
1. Givenatimeperiod,learnauser’sprofilesfromcorrespondinguseractivitygraph
2. Exploituserprofilestoforecastnextactivitytype
¨ Datao Mobileactivitycheckin data
ofNYCandTokyo
¨ EvaluationMetricso Theprecision@N ofactivity
categorypredictiono Theprecision@N ofnew
activityrecommendation¨ Baselines
o Autoencodero DeepWalk: usetruncated
randomwalkstolearnlatentrepresentations
o LINE: preservebothlocalandglobalnetworkstructureswithanedge-samplingalgorithm
o CNN: Convolutional NeuralNetwork
Overall Comparisons on New York and Tokyo Activity Check-in Data18
• Ourmodelachievesthebestperformancesonuserprofiling
• Substructures inagraphare essential for userbehaviorpatterns
Applythelearnedrepresentations topredictnextactivitytype(nextPOIcategory)
Study of Node and Circle Substructures19
¨ EvaluationMetricso Theprecision@N ofactivity
categoryprediction
o Theprecision@N ofnewactivityrecommendation
¨ Baselineso StructRL:considernodeand
circlesubstructureso StructRL-Node:only
considernodesubstructures
o StrucctRL-Circle:onlyconsidercirclesubstructure
¨ Findingso Circlesubstucture aremore
effectiveo Capturingmoresubgraph
topologiescanhelp
Conclusion
¨ Research Problem¨ Learn to profile users by both considering general interests and specific
interests for certain activity types
¨ Method¨ Users as Activity Graphs¨ Formulate modeling specific interests as preserving substructures of user
activity graphs¨ Propose an adversarial substructured learning model to integrate substructure
into representation learning
¨ Take Away Messages¨ Adversarial learning plays the role of regularization¨ Substructure is very important for quantifying user behavior patterns¨ Pre-train neural networks to approximate undifferentiable algorithms¨ Circle is more effective than independent vertexes for profiling users
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Will The Traditional Solution Work?24
Walmart
Hospital
Costco
Gas Station
PreSchool
Gym
Home
Zoo
Sixflag
RegalCinemas
T J MaxxALDI
Farmer Market
PetSmart
Pharmacy
Library
Office Hospital
Costco
Gas Station
MacDonaldWalmart
PreSchool
Auto Service
Home
Zoo
Sixflag
PizzaHut
RegalCinemas
Lab
Library IT
Department
Topologies,contents,locationsofsubgraphswilldynamicallychangeoverusers
Will The Traditional Solution Work?
0 0 1 1
0 0 1 1
0 0 0 0
0 0 0 0
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0 0 0 0
0 0 0 0
0 1 1 0
0 1 1 0
Example:Dynamicbinaryindicatorofsubgraphsintheactivitymatrix/graphsoftwousers
Robustness Check15
@5 @10 @15 @20
Prec
isio
n@N
0.0
0.1
0.2
0.3
0.412 Apr. 2012 −− 12 Jun. 201213 Jun. 2012 −− 13 Aug. 201214 Aug. 2012 −− 14 Oct. 201215 Aug. 2012 −− 15 Oct. 201216 Oct. 2012 −− 16 Feb. 2013
@5 @10 @15 @20
Prec
isio
nnew@
N
0.00
0.05
0.10
0.15
0.2012 Apr. 2012 −− 12 Jun. 201213 Jun. 2012 −− 13 Aug. 201214 Aug. 2012 −− 14 Oct. 201215 Aug. 2012 −− 15 Oct. 201216 Oct. 2012 −− 16 Feb. 2013
@5 @10 @15 @20
Prec
isio
n@N
0.0
0.1
0.2
0.3
0.4
0.5Group 1Group 2Group 3
Group 4Group 5
@5 @10 @15 @20
Prec
isio
nnew@
N
0.00
0.05
0.10
0.15
0.20
0.25
0.3012 Apr. 2012 −− 12 Jun. 201213 Jun. 2012 −− 13 Aug. 201214 Aug. 2012 −− 14 Oct. 201215 Aug. 2012 −− 15 Oct. 201216 Oct. 2012 −− 16 Feb. 2013
New York
Tokyo
• Theperformancesofourmethodcanachieveasmallvarianceandarerelativelystable,especiallywhenKissmall.
¨ Five Periodso 12Apr.2012– 12Jun.2012o 13Jun.2012– 13Aug.2012o 14Aug.2012– 14Oct.2012o 15Aug.2012– 15Oct.2012o 16Oct.2012– 16Feb.2013
¨ Predictiono setthelastday’sactivitiesofeach
timeperiodasapredictivetarget