Figureb2b程序
b2b程序 时间:2021-04-13 阅读:()
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)
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)
pacificrack：VPS降价，SSD价格下降
之前几个月由于CHIA挖矿导致全球固态硬盘的价格疯涨，如今硬盘挖矿基本上已死，硬盘的价格基本上恢复到常规价位，所以，pacificrack决定对全系Cloud server进行价格调整，降幅较大，“如果您是老用户，请通过续费管理或升级套餐，获取同步到最新的定价”。官方网站：https://pacificrack.com支持PayPal、支付宝等方式付款VPS特征：基于KVM虚拟，纯SSD raid...
pigyun25元/月，香港云服务器仅起;韩国云服务器，美国CUVIP
pigyun怎么样?PIGYun成立于2019年，2021是PIGYun为用户提供稳定服务的第三年，期待我们携手共进、互利共赢。PIGYun为您提供：香港CN2线路、韩国CN2线路、美西CUVIP-9929线路优质IaaS服务。月付另有通用循环优惠码：PIGYun，获取8折循环优惠(永久有效)。目前，PIGYun提供的香港cn2云服务器仅29元/月起;韩国cn2云服务器仅22元/月起;美国CUVI...
wordpress高级跨屏企业主题 wordpress绿色企业自适应主题
wordpress高级跨屏企业主题，通用响应式跨平台站点开发，自适应PC端+各移动端屏幕设备，高级可视化自定义设置模块+高效的企业站搜索优化。wordpress绿色企业自适应主题采用标准的HTML5+CSS3语言开发，兼容当下的各种主流浏览器： IE 6+(以及类似360、遨游等基于IE内核的)、Firefox、Google Chrome、Safari、Opera等;同时支持移动终端的常用浏览器应...
b2b程序为你推荐
-
操作http您的appleaccessdenied上网时电脑上显示access denied 是怎么回事php计划任务windows系统下如何设置PHP定时任务sqlserver数据库sql server数据库是什么 型数据库flashftp下载《蔓蔓青萝(全)》.TXT_微盘下载欢迎光临本店鸡蛋蔬菜饺子每个10个3元，牛肉蔬菜饺子每10个5元，欢迎光临本店！ 汉译英如何发帖子网上怎么发帖子？站点管理有关站点的知识介绍？开源网店开源网店系统 独立网店系统 淘宝 有什么区别？