collaborative filtering for recommendation systems in python, nicolas hug

Anintroductionto

COLLABORATIVEFILTERINGINPYTHON

andanoverviewofSurprise

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http://surpriselib.com/

WHATSHOULDIREAD?

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WHATSHOULDISEE?

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WHERESHOULDIEAT?

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TAXONOMYContent-basedLeverageinformationabouttheitemscontentBasedonitemprofiles(metadata).

CollaborativefilteringLeveragesocialinformation(user-iteminteractions)E.g.:recommenditemslikedbymypeers.Usuallyyieldsbetterresults.

Knowledge-basedLeverageusersrequirementsUsedforcars,loans,realestate...Verytask-specific.

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CollaborativefilteringLeveragesocialinformation(user-iteminteractions)E.g.:recommenditemslikedbymypeers.Usuallyyieldsbetterresults.

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RATINGPREDICTION

Rowsareusers,columnsareitems~99%oftheentriesaremissingYourmission:predictthe

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ABOUTMENicolasHugPhDStudent(UniversityofToulouseIII)NeededaPythonlibraryforRSresearch...Didit.

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OUTLINE1. THENEIGHBORHOODMETHOD(K-NN)

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OUTLINE1. THENEIGHBORHOODMETHOD(K-NN)2. OVERVIEWOF

( )

SURPRISEhttp://surpriselib.com

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( )


3. MATRIXFACTORIZATION

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1. THENEIGHBORHOODMETHOD(K-NN)

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NEIGHBORHOODMETHODS

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NEIGHBORHOODMETHODWehaveahistoryofpastratingsWeneedtopredictAlice'sratingforTitanic

1. FindtheusersthathavethesametastesasAlice(usingtheratinghistory)

2. AveragetheirratingsforTitanic

That'sit10


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WENEEDASIMILARITYMEASURE

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numberofcommonrateditems

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numberofcommonrateditemsaverageabsolutedifferencebetweenratings(it'sactuallya

distance)

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distance)

cosineanglebetween and

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distance)

cosineanglebetween andPearsoncorrelationcoefficientbetween and

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distance)

cosineanglebetween andPearsoncorrelationcoefficientbetween and

About3millionsothermeasures11




That'sit12


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AGGREGATINGTHENEIGHBORS'RATINGS

AliceisclosetoBob.HerratingforTitanicisprobablyclosetoBob's(1).So

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AGGREGATINGTHENEIGHBORS'RATINGS

AliceisclosetoBob.HerratingforTitanicisprobablyclosetoBob's(1).SoButweshouldofcourselookatmorethanoneneighbor!

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LET'SCODE!defpredict_rating(u,i):'''Returnestimatedratingofuseruforitemi.rating_histisalistoftuples(user,item,rating)'''

#Retrieveusershavingratedineighbors=[(sim[u,v],r_vj)for(v,j,r_vj)inrating_histif(i==j)]#Sortthembysimilaritywithuneighbors.sort(key=lambdatple:tple[0],reversed=True)#Computeweightedaverageofthek-NN'sratingsnum=sum(sim_uv*r_vifor(sim_uv,r_vi)inneighbors[:k])denum=sum(sim_uvfor(sim_uv,_)inneighbors[:k])

returnnum/denum

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That'sit... 15




That'sit...?15

THEREARE(APPROXIMATELY)HALFABILLIONVARIANTS

Normalizetheratings

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NormalizetheratingsRemovebias(someusersaremean)

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NormalizetheratingsRemovebias(someusersaremean)Useafancieraggregation

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NormalizetheratingsRemovebias(someusersaremean)UseafancieraggregationDiscountsimilarities(givethemmoreorlessconfidence)

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NormalizetheratingsRemovebias(someusersaremean)UseafancieraggregationDiscountsimilarities(givethemmoreorlessconfidence)Useitem-itemsimilarityinstead

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NormalizetheratingsRemovebias(someusersaremean)UseafancieraggregationDiscountsimilarities(givethemmoreorlessconfidence)Useitem-itemsimilarityinsteadOrusebothkindsofsimilarities!

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NormalizetheratingsRemovebias(someusersaremean)UseafancieraggregationDiscountsimilarities(givethemmoreorlessconfidence)Useitem-itemsimilarityinsteadOrusebothkindsofsimilarities!Clusterusersand/oritems

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NormalizetheratingsRemovebias(someusersaremean)UseafancieraggregationDiscountsimilarities(givethemmoreorlessconfidence)Useitem-itemsimilarityinsteadOrusebothkindsofsimilarities!Clusterusersand/oritemsLearnthesimilarities

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NormalizetheratingsRemovebias(someusersaremean)UseafancieraggregationDiscountsimilarities(givethemmoreorlessconfidence)Useitem-itemsimilarityinsteadOrusebothkindsofsimilarities!Clusterusersand/oritemsLearnthesimilaritiesBlahblahblah...

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( )3. MATRIXFACTORIZATION


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2. OVERVIEWOF( )


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SURPRISEAPythonlibraryforrecommendersystems

(Orrather:aPythonlibraryforratingpredictionalgorithms)

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WHY?NeededaPythonlibforquickandeasyprototypingNeededtocontrolmyexperiments

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SOWHYNOTSCIKIT-LEARN?

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SOWHYNOTSCIKIT-LEARN?

Ratingprediction≠regressionorclassification

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YES:)

NO:(

clf=MyClassifier()clf.fit(X_train,y_train)clf.predict(X_test)

rec=MyRecommender()rec.fit(X_train,y_train)rec.predict(X_test)

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BASICUSAGEfromsurpriseimportKNNBasicfromsurpriseimportDataset

data=Dataset.load_builtin('ml-100k')#downloaddatasettrainset=data.build_full_trainset()#buildtrainset

algo=KNNBasic()#usebuilt-inpredictionalgorithm

algo.train(trainset)#fitdata

algo.predict('Alice','Titanic')algo.predict('Bob','ToyStory')#...

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MAINFEATURESEasydatasethandling

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DATASETHANDLING#Built-indatasets(movielens,Jester)data=Dataset.load_builtin('ml-100k')

#Customdatasets(fromfile)file_path=os.path.expanduser('./my_data')reader=Reader(line_format='useritemratingtimestamp',sep='\t')data=Dataset.load_from_file(file_path,reader=reader)

#Customdatasets(frompandasdataframe)reader=Reader(rating_scale=(1,5))data=Dataset.load_from_df(df[['uid','iid','r_ui']],reader)

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MAINFEATURES

Built-inpredictionalgorithms(SVD,k-NN,manyothers)Built-insimilaritymeasures(Cosine,Pearson...)

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BUILT-INALGORITHMSANDSIMILARITIES

SVD,SVD++,NMF, -NN(withvariants),SlopeOne,somebaselines...Cosine,Pearson,MSD(betweenusersoritems)sim_options={'name':'cosine','user_based':False,'min_support':10}algo=KNNBasic(sim_options=sim_options)

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MAINFEATURES

Customalgorithmsareeasytoimplement

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CUSTOMPREDICTIONALGORITHMclassMyRatingPredictor(AlgoBase):

defestimate(self,u,i):return3

algo=MyRatingPredictor()algo.train(trainset)pred=algo.predict('Alice','Titanic')#willcallestimate

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deftrain(self,trainset):'''Fityourdatahere.'''

self.mean=np.mean([r_uifor(u,i,r_ui)intrainset.all_ratings()])

defestimate(self,u,i):returnself.mean

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deftrain(self,trainset):'''Fityourdatahere.'''

self.mean=np.mean([r_uifor(u,i,r_ui)intrainset.all_ratings()])

defestimate(self,u,i):returnself.mean

trainsetobject:Iteratorsovertheratinghistory(user-wise,item-wise,orunordered)IteratorsoverusersanditemsidsOtherusefulmethods/attributes

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classMyKNN(AlgoBase):

deftrain(self,trainset):self.trainset=trainsetself.sim=...#Computesimilaritymatrix

defestimate(self,u,i):

neighbors=[(self.sim[u,v],r_vi)for(v,r_vj)inself.trainset.ur[u]]neighbors=sorted(neighbors,key=lambdatple:tple[0],reverse=True)

sum_sim=sum_ratings=0for(sim_uv,r_vi)inneighbors[:self.k]:sum_sim+=simsum_ratings+=sim*r

returnsum_ratings/sum_sim

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MAINFEATURES

Cross-validation,grid-search

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TYPICALEVALUATIONPROTOCOL

Hidesomeofthe (theybecome )

Usetheremaining topredictthe

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RMSE=

Theaverageerror,wherebigerrorsareheavilypenalized(squared)

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RMSE=

Theaverageerror,wherebigerrorsareheavilypenalized(squared)Dothatmanytimeswithdifferentsubsets:thisiscross-validation

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CROSSVALIDATIONdata=Dataset.load_builtin('ml-100k')#downloaddatasetdata.split(n_folds=3)#3-foldscrossvalidation

algo=SVD()

fortrainset,testsetindata.folds():algo.train(trainset)#fitdatapredictions=algo.test(testset)#predictratingsaccuracy.rmse(predictions,verbose=True)#printRMSE

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GRID-SEARCHparam_grid={'n_epochs':[5,10],'lr_all':[0.002,0.005],'reg_all':[0.4,0.6]}grid_search=GridSearch(SVD,param_grid,measures=['RMSE','FCP'])

data=Dataset.load_builtin('ml-100k')data.split(n_folds=3)

grid_search.evaluate(data)#willevaluateeachcombination

print(grid_search.best_score['RMSE'])print(grid_search.best_params['RMSE'])algo=grid_search.best_estimator['RMSE'])

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MAINFEATURES

Built-inevaluationmeasures(RMSE,MAE...)

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EVALUATIONTOOLS

CanalsocomputeRecall,Precisionandtop-Nrelatedmeasureseasily

fortrainset,testsetindata.folds():algo.train(trainset)#fitdatapredictions=algo.test(testset)#predictratings

accuracy.rmse(predictions)accuracy.mae(predictions)accuracy.fcp(predictions)

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MAINFEATURES

Modelpersistence

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MODELPERSISTENCE

Canalsodumpthepredictionstoanalyzeandcarefullycomparealgorithmswithpandas

#...gridsearchalgo=grid_search.best_estimator['RMSE'])

#dumpalgorithmfile_name=os.path.expanduser('~/dump_file')dump.dump(file_name,algo=algo)

#...#ClosePython,gotobed#...

#Wakeup,reloadalgorithm,profit_,algo=dump.load(file_name)

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( )3. MATRIXFACTORIZATION


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3. MATRIXFACTORIZATION

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MATRIXFACTORIZATION

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MATRIXFACTORIZATIONAlotofhypeduringtheNetflixPrize(2006-2009:improveoursystem,getrich)ModeltheratingsinaninsightfulwayTakesitsrootindimensionalreductionandSVD

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BEFORESVD:PCAHereare400greyscaleimages(64x64):

Putthemina400x4096matrix :

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PCAalsogivesyouthe .

Inadvance:youdon'tneedallthe400guys

BEFORESVD:PCAPCAwillreveal400ofthosecreepytypicalguys:

Theseguyscanbuildbackalloftheoriginalfaces

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PCAONARATINGMATRIX?SURE!Assumeallratingsareknown

Exactsamething!Wejusthaveratingsinsteadofpixels.

PCAwillrevealtypicalusers.

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PCAONARATINGMATRIX?SURE!Assumeallratingsareknown.Transposethematrix

Exactsamething!PCAwillrevealtypicalmovies.

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PCAONARATINGMATRIX?SURE!Assumeallratingsareknown.Transposethematrix

Exactsamething!PCAwillrevealtypicalmovies.

Note:inpractice,thefactorssemanticisnotclearlydefined.

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SVDISPCA2

PCAon givesyouthetypicalusersPCAon givesyouthetypicalmoviesSVDgivesyoubothinoneshot!

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SVDISPCA2

PCAon givesyouthetypicalusersPCAon givesyouthetypicalmoviesSVDgivesyoubothinoneshot!

isdiagonal,it'sjustascaler.

Thisisourmatrixfactorization!

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THEMODELOFSVD

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THEMODELOFSVD

Rating(Alice,Titanic)willbehighRating(Bob,Titanic)willbelow

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SOHOWTOCOMPUTE AND ?

Columnsof aretheeigenvectorsofColumnsof aretheeigenvectorsofAssociatedeigenvaluesmakeupthediagonalofThereareveryefficientalgorithmsinthewild

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Alternateoption:findthe sandthe sthatminimizethereconstructionerror

(Withsomeorthogonalityconstraints).Therearealsoveryefficientalgorithmsinthewild

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Alternateoption:findthe sandthe sthatminimizethereconstructionerror

(Withsomeorthogonalityconstraints).Therearealsoveryefficientalgorithmsinthewild

So,pieceofcakeright?

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OOPS!

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WEASSUMEDTHAT WASDENSEButit'snot!99%aremissing

and arenotevendefinedSo and arenotdefinedeitherThereisnoSVD

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WEASSUMEDTHAT WASDENSEButit'snot!99%aremissing

and arenotevendefinedSo and arenotdefinedeitherThereisnoSVD

Twooptions:Fillthemissingentrieswithasimpleheuristic"Let'sjustnotgiveadamn"--SimonFunk(ended-uptop-3oftheNetflixPrizeforsometime)

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Densecase:findthe sandthe sthatminimizethetotalreconstructionerror

(Withorthogonalityconstraints)

Sparsecase:findthe sandthe sthatminimizethepartialreconstructionerror

(Forgetaboutorthogonality)

APPROXIMATION

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Densecase:findthe sandthe sthatminimizethetotalreconstructionerror

(Withorthogonalityconstraints)

Sparsecase:findthe sandthe sthatminimizethepartialreconstructionerror

(Forgetaboutorthogonality)

APPROXIMATION

Basicallythesame,exceptwedon'thavealltheratings(andwedon'tcare)

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Wewantthevalue suchthat

isminimal:

ComputeRandomlyinitialize

(dothisuntilyou'refedup)

MINIMIZATIONBYGRADIENTDESCENT

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MINIMIZATIONBY(STOCHASTIC)GRADIENTDESCENT

Ourparameter is forall and .Thefunction tobeminimizedis:

defcompute_SVD():'''FitpuandqitoknownratingsbySGD'''p=np.random.normal(size=F)q=np.random.normal(size=F)foriterinrange(n_max_iter):foru,i,r_uiinrating_hist:err=r_ui-np.dot(p[u],q[i])p[u]=p[u]+learning_rate*err*q[i]q[i]=q[i]+learning_rate*err*p[u]

defestimate_rating(u,i):returnnp.dot(p[u],q[i])

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SOMELASTDETAILSUnbiastheratings,addregularization:youget"SVD":

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SOMELASTDETAILSUnbiastheratings,addregularization:youget"SVD":

Goodol'fashionedMLparadigm:Assumeamodelforyourdata( )FityourmodelparameterstotheobserveddataPraisetheLordandhopeforthebest

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AFEWREMARKSismostlyatoolforalgorithmsthatpredict

ratings.RSdomuchmorethanthat.Focusonexplicitratings(implicitratingsalgorithmswillcome,hopefully)NotaimedtobeacompletetoolforbuildingRSfromAtoZ(checkout )Notaimedtobethemostefficientimplementation(checkout )

Surprise

Mangaki

LightFM

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https://github.com/mangaki/mangaki

https://github.com/lyst/lightfm

SOMEREFERENCESCan'trecommendenough(punintended)Aggarwal'sRecommenderSystems-TheTextbookJeremyKun's (greatinsightson and )blog PCA SVD

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https://jeremykun.com/

https://jeremykun.com/2011/07/27/eigenfaces/

https://jeremykun.com/2016/04/18/singular-value-decomposition-part-1-perspectives-on-linear-algebra/

THANKS!

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collaborative filtering for recommendation systems in python, nicolas hug

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