AndyHsieh,LongqiYang,YinCui,Tsung-YiLin,SergeBelongie,DeborahEstrin

ConnectedExperienceLab,CornellTech

AOL CONNECTED EXPERIENCES LAB CORNELL TECH

CollaborativeMetricLearning

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CollaborativeMetricLearning

• Adifferentperspectiveoncollaborativefiltering

• Betteraccuracy

• ExtremelyefficientTop-Krecommendations

• Easytointerpretandextend

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User-ItemMatrixUsers

Items

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MatrixFactorization(MF)

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Users

Items

Users

Items

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ImplicitFeedback

• Ubiquitousintoday’sonlineservices

• Onlypositivefeedbackisavailable

• TraditionalMFdoesnotwork

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Click Thumbsup Like

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MatrixFactorizationforImplicitFeedback

• WeightedRegularizedMatrixFactorization(WRMF)[Hu08]

• ProbabilisticMatrixFactorization(PMF)[Salakhutdinov08]

• BayesianPersonalizedRanking(BPR)[Rendle09]

andmanymore…

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ThinkBeyondMatrix

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• Nolongeraboutestimatingratings

• Butaboutmodelingtherelationships

betweendifferentuser/itempairs

Explicit Implicit

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ThinkBeyondMatrix

• Nolongeraboutestimatingratings

• Butaboutmodelingtherelationships

betweendifferentuser/itempairs

Explicit Implicit

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MetricLearning

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Knownrelationships

Unknownrelationships

CollaborativeMetricLearning

• Learnajointuser-itemdistancemetric.

• TheEuclideandistancesreflecttherelationshipsbetweenusers/items.

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BasedontheinherentTriangularInequalityofMetricLearning– IfAisclosetoB,andBisclosetoC,thenAisclosetoC.

• Fitthemodelwithimplicitfeedback

1. Anuserispulledclosertotheitemssheliked

2. Othersimilarusersarepulledcloser.

3. Theitemsuserslikedarealsopulledcloser.

• Top-KrecommendationsaresimplyKNN

search(awell-optimizedtask)

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BasedontheinherentTriangularInequalityofMetricLearning– IfAisclosetoB,andBisclosetoC,thenAisclosetoC.

• Fitthemodelwithimplicitfeedback

1. Anuserispulledclosertotheitemssheliked

2. Othersimilarusersarepulledcloser.

3. Theitemsuserslikedarealsopulledcloser.

• Top-KrecommendationsaresimplyKNN

search(awell-optimizedtask)

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BasedontheinherentTriangularInequalityofMetricLearning– IfAisclosetoB,andBisclosetoC,thenAisclosetoC.

• Fitthemodelwithimplicitfeedback

1. Anuserispulledclosertotheitemssheliked

2. Othersimilarusersarepulledcloser.

3. Theitemsuserslikedarealsopulledcloser.

• Top-KrecommendationsaresimplyKNN

search(awell-optimizedtask)

CollaborativeLargeMarginNearestNeighbor

User

Positiveitem

Imposter

SafetyMargin

Gradients

Before After

*TheoutlineoffigureisinspiredbyWeinberger,KilianQ.,JohnBlitzer,andLawrenceSaul."Distancemetriclearningforlargemarginnearestneighborclassification." Advancesinneuralinformationprocessingsystems 18(2006):1473. 14

PitfallsofMatrixFactorization(Dot-Product)

• Dot-Productviolatestriangleinequalitymisleadingembedding.

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PitfallsofMatrixFactorization(Dot-Product)

• Dot-Productviolatestriangleinequalitymisleadingembedding.

𝑉#$𝑉% = 0: doesnotreflectthattheyarebothlikedby𝑈*

𝑈#$𝑈% = 0:doesnotreflectthattheybothsharethesameinterestas𝑈*

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CollaborativeMetricLearningEmbedding

• Euclidiandistancefaithfullyreflectstherelativerelationships.

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IntegratingItemFeatures

• Usealearnablefunction(e.g.

Multi-LayerPerceptron)to

projectfeaturesintouser-item

embedding.

• Treattheprojectionsasaprior

foritems'locations.

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Evaluation

• 6DatasetsfromDifferentDomains

• Papers - CiteULike

• Books - BookCrossing

• Photography - Flickr

• Articles - Medium

• Movies - MovieLens

• Music - EchoNest

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Accuracy(Recall@50)

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CiteULike BookCX Flickr Medium MovieLens EchoNest

Recall@50ImprovementsOverBPR(%)

WRMF WARP CML

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*IndicatethatCML>thesecondbestalgorithmisstatisticallysignificantaccordingtoWilcoxonsignedranktest 20

Accuracy(withItemFeatures)

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CiteULike BookCX Flickr Medium MovieLens

VBPR CDL CML+F

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*IndicatethatCML>thesecondbestalgorithmisstatisticallysignificantaccordingtoWilcoxonsignedranktest

Recall@50ImprovementsOverFactorizationMachine(%)

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Efficiency

• AlloptimizedwithLSHs

• CML’sthroughputisimprovedby106x

withonly2%reductioninaccuracy

• Over8xfasterthan(optimized)MF

modelsgiventhesameaccuracy

8xfaster

‘sarebruteforcesearch

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EmbeddingInterpretability

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AB

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B

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Conclusions

• Thenotionofuser-itemmatrixandmatrixfactorizationbecomeslessapplicablewithimplicitfeedback.• CMLisametriclearningmodelthathas• betteraccuracy,efficiency,interpretability,andextensibility.

• Applyingmetric-basedalgorithms,suchasK-means,andSVMs,tootherrecommendationproblems.

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Thankyou!

AOL CONNECTED EXPERIENCES LAB CORNELL TECH