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Enhancing Matrix Factorization Through Initialization for Implicit Feedback Databases

Enhancing Matrix Factorization Through Initialization for Implicit Feedback Databases. Balázs Hidasi Domonkos Tikk Gravity R&D Ltd. Budapest University of Technology and Economics. CaRR workshop , 14th February 2012, Lisbon. Outline. Matrix factoriztion Initialization concept

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Enhancing Matrix Factorization Through Initialization for Implicit Feedback Databases

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  1. Enhancing Matrix Factorization Through Initialization forImplicit Feedback Databases Balázs Hidasi Domonkos Tikk Gravity R&D Ltd. Budapest University of Technology and Economics CaRR workshop, 14th February 2012, Lisbon

  2. Outline • Matrixfactoriztion • Initializationconcept • Methods • Naive • SimFactor • Results • Discussion

  3. MatrixFactorization • CollaborativeFiltering • One of the most commonapproaches • Approximatestheratingmatrixasproduct of low-rankmatrices Items R P Q ≈ Users

  4. Matrixfactorization • Initialize P and Q withsmallrandomnumbers • Teach P and Q • AlternatingLeastSquares • GradientDescent • Etc. • Transformsthedatato a featurespace • Separatelyforusers and items • Noisereduction • Compression • Generalization

  5. Implicit feedback • No ratings • User-iteminteractions (events) • Muchnoisier • Presence of an event mightnot be positivefeedback • Absence of an event doesnotmeannegativefeedback • No negativefeedback is available! • More commonproblem • MF for implicit feedback • Less accurateresultsduetonoise • Mostly ALS is used • Scalabilityproblems (ratingmatrix is dense)

  6. Concept • Good MF model • The featurevectors of similarentitiesaresimilar • Ifdata is toonoisy similarentitieswon’t be similarbytheirfeatures • Start MF from a „good” point • Featurevectorsimilaritiesare OK • Data is more thanjustevents • Metadata • Infoaboutitems/users • Contextualdata • Inwhatcontextdidtheeventoccured • Canweincorporatethosetohelp implicit MF?

  7. Naiveapproach • Describeitemsusinganydatawehave (detailedlater) • Long, sparsevectorsforitemdescription • Compressthesevectorstodensefeaturevectors • PCA, MLP, MF, … • Length of desiredvectors = Numberoffeaturesin MF • Usethesefeaturesas starting points

  8. Naiveapproach • Compression and alsonoisereduction • Doesnotreallycareaboutsimilarities • Butoftenfeaturesimilaritiesarenotthatbad • If MF is used • Half of theresults is thrown out Descriptors Descriptorfeatures Description of items Itemfeatures ≈ Items

  9. SimFactoralgorithm • Trytopreservesimilaritiesbetter • Starting from an MF of itemdescription • Similarities of items: DD’ • Somemetricsrequiretransformationon D Descriptors Descriptorsfeatures Description of items (D) Itemfeatures ≈ Items Description of items (D’) Itemsimilarities (S) Description of items (D) =

  10. SimFactoralgorithm • Similarityapproximation • Y’Y  KxKsymmetric • Eigendecomposition Itemfeatures (X’) Itemsimilarities (S) Itemfeatures (X) Descriptorsfeatures (Y’) ≈ Descriptorsfeatures (Y) Itemfeatures (X’) Itemsimilarities (S) Itemfeatures (X) Y’Y ≈

  11. SimFactoralgorithm • λ diagonal λ = SQRT(λ) * SQRT(λ) • X*U*SQRT(λ) = (SQRT(λ)*U’*X’)’=F • F is MxKmatrix • S  F * F’  Fusedforinitialization Y’Y U λ U’ = Itemfeatures (X’) Itemsimilarities (S) Itemfeatures (X) U SQRT (λ) SQRT (λ) U’ ≈ F’ Itemsimilarities (S) F ≈

  12. Creatingthedescriptionmatrix • „Any” dataabouttheentity • Vector-spacereprezentation • ForItems: • Metadatavector (title, category, description, etc) • Eventvector (whoboughttheitem) • Context-statevector (inwhichcontextstatewasitbought) • Context-event (inwhichcontextstatewhoboughtit) • ForUsers: • Allaboveexceptmetadata • Currently: Chooseonesourcefor D matrix • Contextused: seasonality

  13. Experiments: Similaritypreservation • Real life dataset: online grocery shopping events • SimFactorapproximatessimilaritiesbetter

  14. Experiments: Initialization • Usingdifferentdescriptionmatrices • And bothnaiveandSimFactorinitialization • Baseline: random init • Evaluationmetric: recall@50

  15. Experiments: Grocery DB • Upto 6% improvement • Best methodsuseSimFactor and usercontextdata

  16. Experiments: „Implicitized” MovieLens • Keeping 5 starratings implicit events • Upto 10% improvement • Best methodsuseSimFactor and itemcontextdata

  17. Discussion of results • SimFactoryieldsbetterresultsthannaive • Contextinformationyieldsbetterresultsthanotherdescriptions • Contextinformationseparateswellbetweenentities • Grocery: Usercontext • People’sroutines • Differenttypes of shoppingsindifferenttimes • MovieLens: Itemcontext • Differenttypes of movieswatchedondifferenthours • Context-basedsimilarity

  18. Whycontext? • Groceryexample • Correlationbetweencontextstatesbyusers low • Whycancontextawarealgorithms be efficient? • Differentrecommendationsindifferentcontextstates • Contextdifferentiateswellbetweenentities • Easiersubtasks

  19. Conclusion & Futurework • SimFactor Similaritypreservingcompression • Similaritybased MF initialization: • Descriptionmatrixfromanydata • ApplySimFactor • Use output asinitialfeaturesfor MF • Contextdifferentitatesbetweenentitieswell • Futurework: • Mixed descriptionmatrix (multipledatasources) • Multipledescriptionmatrix • Usingdifferentcontextinformation • Usingdifferentsimilaritymetrics

  20. Thanksforyourattention! For more of my recommender systems related research visit my website: http://www.hidasi.eu

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