推荐系统-国科大-7+Item-Item+CF

RecommenderSystems,罗平luop,IntroductiontoItem-itemCollaborativefiltering,7-1,2,LearningObjectives,Tounderstandthemotivation,history,andintuitionbehinditem-itemCFalgorithmsTogainabasicunderstandingofthealgorithmidea,preparingyoutomasterthedetailslaterthismoduleTounderstandsomeofthepracticalstrengthsandweaknessesofthealgorithm,3,KeyReferences,BadrulSarwar,GeorgeKarypis,JosephKonstan,andJohnRiedl.Item-basedCollaborativeFilteringRecommendationAlgorithms.WWW,2001.MukundDeshpande,GeorgeKarypis:Item-basedtop-Nrecommendationalgorithms.ACMTrans.Inf.Syst.22(1):143-177(2004),4,Motivation(1),User-UserCFwasgreat,exceptLowcoverageWithlargeitemsets,smallnumbersofratings,toooftentherearepointswherenorecommendationcanbemade,5,Motivation(2),ComputationalperformanceWithmillionsofusers(ormore),computingall-pairscorrelationsisexpensiveEvenincrementalapproacheswereexpensiveAnduserprofilescouldchangequicklyneededtocomputeinrealtimetomakeusershappy,6,Item-ItemSimilarity,Item-ItemsimilarityisfairlystableDependentonhavingmanymoreusersthanitemsAverageitemhasmanymoreratingsthananaverageuserIntuitively,itemsdontgenerallychangerapidlyatleastnotinratingsspaceItemsimilarityisaroutetocomputingapredictionofausersitempreference,7,8,?,Alittlemoredetail,Twostepprocess:ComputesimilaritybetweenpairsofitemsPredictuser-itemratingWeightedsumofrated“item-neighbors”,9,BenefitsofItem-Item,ItactuallyworksquitewellGoodMAEperformanceonprediction;goodrankperformanceontop-NEfficientimplementationAtleastincaseswhere|U|I|Benefitsofpre-computability,10,ComparisonbetweenUUandII,UUCFGettinginformationfromthesubsetofpeoplewhomostshareyourtastesIICFTogettinginformationfrompotentiallyeverybodyinthecommunity,butfilteredsothattherateditems,whicharesimilartothetargetitem,areconsidered,11,Item-ItemCFalgorithm,7-2,12,FrameworkofItem-ItemCF,Pre-computeitemsimilaritiesoverallpairsofitemsLookforitemssimilartothosetheuserlikesOrhaspurchasedOrhasintheirbasket,13,PickingItemNeighbors,TopredicttheratingofuseruonitemiForauseruItemset_u:thesetofitemsuseruhasratedForitemiNeighbor_i:thesetofitemswhicharetop-ksimilartoitemiTheintersectionofItemset_uandNeighbor_i,14,PickingNeighbors,Twosmall:intersectionofItemset_uandNeighbor_iGiveuppredictionGoodvalueofkimportantktoolargetoomuchnoise(low-similarityitems)ktoosmalllowcoveragek=20oftenworkswell,15,ScoringItems,ForeachitemtoscoreFindthesimilaritemstheuserhasratedComputetheweightedaverageofusersratings,16,ARevisittoUser-UserVariationsandTuning,SimilaritiesSignificanceweightingVarianceweightingConsideringtheratingvarianceforanitemSelectingneighborhoodsNormalizingratings,17,Applythisanalysisframeworktoitem-itemcollaborativefiltering,ItemSimilarity,FortwoitemratingvectorsNormalizationfirst:subtractusermeanCosinesimilarity,18,SignificanceWeighting,MaynotbetoousefulSincethenumberofratingsoneachitemisrelativelylarge,19,VarianceWeighting:UserWeight,ConsidertheratingvarianceforeachuserHowtoadapttheCosinesimilarity,20,AnotherUserWeight:UserTrust,Goal:incorporateusertrustworthinessintoitemrelatednesscomputationUsersglobalreputation,notper-usertrustSolutionweightusersbytrustbeforecomputingitemsimilaritiesHigh-trustusershavemoreimpact,21,MassaandAvesani.2004.Trust-AwareCollaborativeFilteringforRecommenderSystems.,SelectingItemNeighbors,Similarsolution,22,Normalization,Notneeded,23,CoreAssumptions/Limitations,Item-itemrelationshipsYourtasteobeysthatofmostpeopleMainlimitation/complaint:lowerserendipityShowacasewheretheresultsfromUU-CFandII-CFaretotallydifferent,24,Conclusion,Item-itemisefficientandstraightforwardAfewparametersneedtuningforspecificdata,domainLimitation:lowerserendipity,25,Assignment,ShowacasewheretheresultsfromUU-CFandII-CFaretotallydifferent随机点名号码:5位同学冯晴晨、彭新亮、杨溪远、韩啸、吴宇文下次课，5位同学中的2位上台表演,26,Item-itemCollaborativeFilteringonunarydata,7-3,27,User-ItemInteractiveData,RatingUnarydata(implicitfeedback)clicksplayspurchasesCountingdata,28,UnaryData,Wevetalkedaboutitem-itemoverratingdataUnarydata(implicitfeedback)clicksplayspurchasesButsometweaksareneeded,29,UnaryData:DataRepresentation,NeedsomematrixtorepresentdataLogical(1/0)user-itempurchasematrixNotpurchase:0PU(PositiveandUnlabeled)dataPurchasecountmatrixLogfunctiononcounts,30,UnaryData:DataNormalization,Normalizeuservectorstounitvecto