推荐系统-国科大-5-User-User+Collaborative+Filtering

RecommenderSystems,罗平luop,IntroductiontoUser-UserCollaborativeFiltering,5-1,2,LearningObjectives,Tounderstandtheintuitionandhistoryoftheuser-usercollaborativefilteringalgorithmToreviewthebasicideasandassumptions(andthereforelimitations)behindthealgorithm,3,History,1992:InformationTapestry,DougTerry,XeroxParc1994-1995:EarlyAutomatedCFSystemsGroupLens(fornews),MinnesotaandMITRingo(formusic),MIT,4,ACMSoftwareSystemAward(2010),FortheGroupLensCollaborativeFilteringRecommenderSystems,whichshowedhowtoautomatetheprocessbywhichadistributedsetofuserscouldreceivepersonalizedrecommendationsbysharingratings,leadingtobothcommercialproductsandextensiveresearch.,5,/award_winners/riedl_2663490.cfm,CoreAssumptions/Limitations,Assumption:OurpastagreementpredictsourfutureagreementBaseAssumption#1:OurtastesareeitherindividuallystableormoveinsyncwitheachotherBaseAssumption#2:OursystemisscopedwithinadomainofagreementPolitics,humor,technology,6,BreakingDownUser-UserCollaborativeFiltering,5-2,7,KeyReference,PaulResnick,NeophytosIacovou,MiteshSuchak,PeterBergstrom,andJohnRiedl.GroupLens:AnOpenArchitectureforCollaborativeFilteringofNetnews.CSCW,1994AnAlgorithmicFrameworkforCollaborativeFilteringbyHerlocker,Konstan,Borchers,RiedlProc.SIGIR1999,8,RatingMatrix,MatrixRR_ui:theratingfromuseruonitemIAverysparsematrixQuestionToinferthevaluesintheemptycells,9,JustAverage,Non-Personalized,10,RatingNormalization,Non-Personalized,Maybeoutoftheratingscale,11,RatingNormalization,Personalized,12,Howtoselecttheneighborhoodsbarr_uistheaveragevalueoveralltheratingsofuRemovetheneighborswithnegativeagreementvalues,PearsonCorrelationCoefficient,13,Here,barr_uistheaveragevalueovertheratingsofuontheitemsbothuandahaverated,AlgorithmforU-UCF,ForauseruComputeitssimilarityvaluestoalltheotherusersIdentifyitsnearestneighborsWiththenearestneighbors,foreachitemiPredictr_uitotheweightedsumoftheratingsonitemifromtheneighbors,14,IssuesonU-UCF,LowcoverageForanitem,onwhichallthenearestneighborshavefewratings,15,ImplementationIssues,GivenmusersandnitemsComputationcanbeabottleneckCorrelationbetweentwousersisO(n)AllcorrelationsforauserisO(mn)AllpairwisecorrelationsisO(m2n)LotsofwaystomakemorepracticalMorepersistentneighborhoodsCachedorincrementalcorrelations,16,User-UserVariationsandTuning,SimilaritiesSignificanceweightingVarianceweightingConsideringtheratingvarianceforanitemSelectingneighborhoodsNormalizingratings,17,ComputingSimilarities,PearsoncorrelationSpearmanrankcorrelationHasntbeenfoundtoworkaswellhereCosineSimilarity,18,SignificanceWeighting,Considerthenumberofco-rateditemsmultiplybymin(n,50)/50nisthenumberofcommonratings50isthecutoffnumber,19,ConsideringtheRatingVarianceforanItem,Varianceweighting,20,Z-scorebased,NormalizingRatings,Why?,UsersratedifferentlySomeratehigh,otherslowAveragingignoresthesedifferencesNormalizationcompensatesforthem,21,RatingNormalization:Mean-centering,Maybeoutoftheratingscale,22,RatingNormalization:z-scorenormalization,23,SelectingNeighborhoods,ThresholdsimilarityTop-NneighborsbysimilarityCombined,24,HowManyNeighbors?,Intheory,themorethebetterIfwehaveagoodsimilaritymeasureInpractice,noisefromdissimilarneighborsdecreasesusefulnessBetween25and100isoftenusedFewerneighborslowercoverageUsethesamegroupofneighborsfordifferentitemsGiveuppersonalizedrecommendationiftheneighborsdonothaveenoughratingsonthetargetitem,25,GoodConfigurations,SimilaritiesPearsoncorrelation,SpearmanrankingcorrelationSignificanceweightingNeededVarianceweightingDoes