Figureb2b程序

AHybridB2BAppRecommenderSystemAlexandruOprea1,ThomasHornung2,Cai-NicolasZiegler3,HolgerEggs1,andGeorgLausen21SAPCommercialPlatform,St.
Leon-Rot&SAPResearch,Darmstadt,Germany{alexandru.
dorin.
oprea,holger.
eggs}@sap.
com2InstituteofComputerScience,Albert-Ludwigs-Universit¨atFreiburg,Germany{hornungt,lausen}@informatik.
uni-freiburg.
de3AmericanExpress,PAYBACKGmbH,M¨unchen,Germanycai-nicolas.
ziegler@payback.
netAbstract.
RecommendersystemsareintegraltoB2Ce-commerce,withlittleusesofarinB2B.
WepresentaliverecommendersystemthatoperatesinadomainwhereusersarecompaniesandtheproductsbeingrecommendedB2Bapps.
Besidesoperatinginanentirenewdomain,theSAPStorerecommenderisbasedonaweightedhybriddesign,makinguseofanovelcondence-basedweightingschemeforcombiningratings.
Evaluationshaveshownthatoursystemperformssignicantlybetterthanatop-sellerrecommenderbenchmark.
1IntroductionandMotivationTheSAPStorecaterstoSMEcompaniesthataimtodrivetheirbusinessviaB2Bapps,e.
g.
,forcustomerrelationmanagementorcompliance.
Manyoftheseappsaregearedtowardsspecicindustriesandtheirneeds.
Asthenumberofpartnersproducingthemisgrowing,soisthenumberofappsinthestoreitselfandthusthecomplexityfortheuser(whorepresentsacompany)toactuallyndwhatheislookingfor.
Toactivelyhelptheuser,weproposeahybridrecommendersystemthataddressesexactlytheneedsofthisspecicB2Bscenario.
Thesystemputstousebothknowledge-based,collaborative,andcontent-basedsub-recommenders.
Moreover,wepresentanovelhybridweightingscheme[1]thatincorporatescon-dencescoringforthepredictionsproduced,sothatsub-recommenderscontributeforrecommendationsaccordingtotheircondenceweight.
Thesystemisliveandcanbeusedbylogged-inusers1.
Wehaveconductedempiricalevaluationsviahold-outtestingthatshowthattherecommenderout-performsthenon-personalizedtop-sellerrecommender.
2RecommenderSystemArchitectureThearchitectureoftherecommenderisdepictedinFigure1.
Overall,wehavethreedierentinformationsourcesforgeneratingnewrecommendations:the1Seehttp://store.
sap.
comF.
Daniel,P.
Dolog,andQ.
Li(Eds.
):ICWE2013,LNCS7977,pp.
490–493,2013.
cSpringer-VerlagBerlinHeidelberg2013AHybridB2BAppRecommenderSystem491Knowledge-basedFilter(KBF)UserProfilesAppProfilesTRXDataUser-ItemCFItem-ItemCFContent-basedAugmentationContent-basedAugmentationItem-ItemMatrixUser-UserMatrixWeightedMeanRecommendationList12a2b34Fig.
1.
SAPStorerecommendersystemarchitectureuserproles(e.
g.
,companysize,industry,country),theappproles(e.
g,sup-portedindustries,businessareas),andthetransactionalcustomerdata(e.
g.
,salesorders,downloads).
Initially,theknowledge-basedcomponentltersthelistofrelevantappsbyasetofplausibilityrulesresultinginanunsortedsetofcandidateapps(1).
Thesearefedtoanitem-item(2a)anduser-itemcollaborativelter(CF),see(2b)[2].
Todealwiththecold-startproblemincaseswhereonlysparseratingsareavailableforapps,acontent-basedaugmentationschemecomputessimilaritiesbasedonthecosinesimilaritymeasure[3]betweenpropertiesoftheapps.
Forusersthatarenewtothesystem,thesimilaritycanbedeterminedbycomparingtheirprolestootherusersbasedontheircosinesimilarity.
Thisway,thetwomatriceswillcontainmeaningfulentriesforallusersandappsknowntothesystem,andrecommendationsgetmorepersonalizedoncemorecontextdataisavailable.
ThescoresofthetwoCFalgorithmsarecombinedbyaweightedmean(cf.
Section2.
1),andasortedtop-krecommendationlistisreturned.
Thecalculationofthematricesisdoneo-lineasthecomputationisquadraticinthenumberofusersorapps,respectively.
2.
1WeightingbyCondenceScoresThescoreofarecommendedappisbasedonaweightedmeanoftheconstituentitem-itemanduser-itemscores.
Eachofthesegivesanestimateofhowmuchausermightlikeanapp;e.
g.
,Eq.
1showshowapredictionscorefortheitem-itemcaseisdeterminedforappamforuseru:Theratingsru(b)ofuforappsb∈Ru492A.
Opreaetal.
hehasalreadyratedareweightedbytheirsimilaritytoam,denoteds(b,am),asanindicatorifthisappmightberelevantfortheuser2.
pi(u,am)=b∈Rus(am,b)·ru(b)b∈Rus(am,b)(1)Now,foreachrecommenderscoreacondencescoreiscalculated,denotedciandcurespectively,whichisbasedonthenumberofsupportingitemsorusersofeachprediction.
Theseweightsareusedtodeterminetheoverallscorep:p(u,am)=ci·pi(u,am)+cu·pu(u,am)ci+cu(2)Thecondencescorecuforthepredictionpu(u,am)tellsushowreliableapre-dictionis.
Itgrowswithagrowingnumberofsupportingdatapoints:Foreachuserui,wecalculatethez-scoreofhissimilaritywithourcurrentuseru.
Wenowsumthesez-scoresimilaritiesforallkusersinuseru'sneighborhood[2].
Thesumisdividedbykandtheresultingvaluegivesustheaveragenormal-izedsimilarityofalltheuserswhoseratingshavebeentakenintoaccountforpu(u,am).
Thesameisdonefortheitem-basedcase.
Sincewearemakinguseofstandardz-scores,thelinearcombinationshowninEq.
2basedonthetwocondenceweightsissound.
Thecondenceschemerepre-sentsapowerfulmeanstoadjustthehybridrecommender'sweightingaccordingtothepredictedreliabilityofeachofthetwosub-recommenders.
3PerformanceEvaluationInordertotesttheperformanceofthepresentedhybridrecommenderusingournovelcondence-basedweightingscheme,weconductedanempiricalevaluationwithreal-worlddataof5,233users(e.
g.
,companiesregisteredforandusingtheSAPStore)havingpurchasedorexpressedinterestin615appsolutions.
ThefrequencydistributioninFig.
2(a)showsleadsperapp,i.
e.
,howmanycompanieshavepurchasedorexpressedinterestineachapp,sortedindescendingorder.
Thelog-logplottedgraphexhibitsapower-lawdistribution,soasmallnumberofappsattractsahighnumberofleads.
ThisisconrmedbyFig.
2(b),showingthatthetop-5appsaccumulate29%ofallleads,andtop-100capture90%.
Wethusconjecturethatanon-personalizedtop-sellerrecommender,whichonlyrecommendsthetop-Nmostpopularapps,willperformverywell.
Weadoptedahold-outcross-validationapproachfortesting,whereoneratingrvofauseriswithheldandallothersareusedtodenehisproleandcalcu-latepredictions,aimingtorecommendexactlyrv.
Forbaselining,wecomparedourrecommender'sperformancewiththatofthetop-sellerrecommender.
Theevaluationtaskforeachofthetworecommenderswastoproducealistoftop-Nrecommendationsandcountinhowmanycasestheproducedlistcontainedrv.
TheevaluationisshowninTab.
1.
Allresultsexhibitstatisticalsignicanceatthepη(a)йййййййййййη(b)Fig.
2.
Log-logfrequencydistributionofleadsperapp(a)andcumulativeshareofleadsbynumberofapps(b)Table1.
PerformancebenchmarkresultsTop-1Top-3Top-5Top-10Hybridrecommender10.
9%24.
4%33.
5%51.
2%Top-seller6.
6%18.
9%27.
6%43.
4%4ConclusionandOutlookWehavepresentedourrecommenderforthenewdomainofB2Bapps,makinguseofanovelhybridweightedschemebasedoncondencescoring.
OurrstevaluationshaveshownverypromisingresultsandthesystemhasgoneliveintooperationaluseatSAP.
Inthefuture,wewanttotunetherecommendingalgorithmsfurtherandaimatdoingthematrixcalculationsinreal-time,usingHANA[4],SAP'snewhigh-performancein-memorydatabase.
References1.
Burke,R.
:HybridWebRecommenderSystems.
In:Brusilovsky,P.
,Kobsa,A.
,Nejdl,W.
(eds.
)AdaptiveWeb2007.
LNCS,vol.
4321,pp.
377–408.
Springer,Heidelberg(2007)2.
Adomavicius,G.
,Tuzhilin,A.
:TowardtheNextGenerationofRecommenderSys-tems:ASurveyoftheState-of-the-ArtandPossibleExtensions.
IEEETrans.
Knowl.
DataEng.
17(6),734–749(2005)3.
Baeza-Yates,R.
A.
,Ribeiro-Neto,B.
A.
:ModernInformationRetrieval-TheCon-ceptsandTechnologyBehindSearch,2ndedn.
PearsonEducationLtd.
,Harlow(2011)4.
F¨arber,F.
,May,N.
,Lehner,W.
,Groe,P.
,M¨uller,I.
,Rauhe,H.
,Dees,J.
:TheSAPHANADatabase–AnArchitectureOverview.
IEEEDataEng.
Bull.
35(1),28–33(2012)