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)
UCloud云服务器低至年59元
最近我们是不是在讨论较多的是关于K12教育的问题，培训机构由于资本的介入确实让家长更为焦虑，对于这样的整改我们还是很支持的。实际上，在云服务器市场中，我们也看到内卷和资本的力量，各大云服务商竞争也是相当激烈，更不用说个人和小公司服务商日子确实不好过。今天有看到UCloud发布的夏季促销活动，直接提前和双十一保价挂钩。这就是说，人家直接在暑假的时候就上线双十一的活动。早年的双十一活动会提前一周到十天...
2021年7月最新洛杉矶CN2/香港CN2 vps套餐及搬瓦工优惠码 循环终身优惠6.58%
搬瓦工怎么样?2021年7月最新vps套餐推荐及搬瓦工优惠码整理，搬瓦工优惠码可以在购买的时候获取一些优惠，一般来说力度都在 6% 左右。本文整理一下 2021 年 7 月最新的搬瓦工优惠码，目前折扣力度最大是 6.58%，并且是循环折扣，续费有效，可以一直享受优惠价格续费的。搬瓦工优惠码基本上可能每年才会更新一次，大家可以收藏本文，会保持搬瓦工最新优惠码更新的。点击进入：搬瓦工最新官方网站搬瓦工...
TTcloud：日本独立服务器促销活动,价格$70/月起,季付送10Mbps带宽
ttcloud怎么样?ttcloud是一家海外服务器厂商，运营服务器已经有10年时间，公司注册地址在香港地区，业务范围包括服务器托管，机柜托管，独立服务器等在内的多种服务。我们后台工单支持英文和中文服务。TTcloud最近推出了新上架的日本独立服务器促销活动，价格 $70/月起，季付送10Mbps带宽。也可以跟进客户的需求进行各种DIY定制。点击进入：ttcloud官方网站地址TTcloud拥有自...
b2b程序为你推荐
-
支持ipadapple.com.cn苹果官网序列号查询servererror电脑连接路由登录提示server error:401 N/A，如何处理？flashwind下载了那个FlashWind极速旋风还需要安装吗？怎么安装？怎样使用？access数据库修复请问Access数据库修复恢复该怎么办啊，有些页和模块打不开了，也不知道是怎么回事，丢了文件还开启javascript怎样打开JavaScript?360邮箱免费注册360账号-电子邮箱怎么填写？人人视频总部基地落户重庆重庆总部城的项目简介人人视频总部基地落户重庆迁户口入重庆什么是支付宝支付宝是什么意思？