leadcentos6.0

centos6.0  时间:2021-03-27  阅读:()
TheNewAlgorithmoftheItem-basedonMapReduceZHAOWei1,a1CollegesoftwareTechnologySchool,ZhengzhouUniversityZhengzhou450002,Chinaaiezhaowei@163.
comKeywords:RecommendationsystemparallelcomputingClusteringAbstract.
TraditionalcollaborativefilteringalgorithmbasedonitemandK-meansclusteringalgorithmarestudied,theparallelalgorithmofcollaborativefilteringItem-basedonMapReduceisproposedbyusingMapReduceprogrammingmodel.
Thealgorithmismainlydividedintotwosteps,onestepisK-Meansalgorithmclusteringforusers,anotherstepistheparallelItem-basedalgorithmforclusteringuserrecommendation.
Experimentalresultsshowthatthealgorithmhasobtainedverygoodeffect,improvedtherunningspeedandexecutionefficiency,theimprovedalgorithmismuchsuitableforprocessingbigdata.
IntroductionBigdatausuallyincludesdatasetswithsizesbeyondtheabilityofcommonlyusedsoftwaretoolstocapture,curate,manage,andprocessdatawithinatolerableelapsedtime.
Bigdataishighvolume,highvelocity,and/orhighvarietyinformationassetsthatrequirenewformsofprocessingtoenableenhanceddecisionmaking,insightdiscoveryandprocessoptimization.
Volumemeansbigdatadoesn'tsample;itjustobservesandtrackswhathappens;Velocitymeansbigdataisoftenavailableinreal-time;Varietymeansbigdatadrawsfromtext,images,audio,video;plusitcompletesmissingpiecesthroughdatafusion[1].
Therefore,thebigdatamustbethroughthecomputerstatistics,comparison,analysisofthedatacanbetheobjectiveresults.
Nowelectroniccommercesystemsofeverytransaction,everyinputandeverysearchcanasdata,datathroughthecomputersystemtodothescreening,sorting,analysis,sothattheanalysisresultsisnotonlyanobjectiveconclusion,moreabletohelpbusinessprovidedthedecision-makingofenterprisesandalsocollectedusefuldatacanalsobereasonableplanning,activelyguidethedevelopmentoflargerpowerconsumption,andmoreeffectivemarketingandpromotion.
Withtheincreasingamountofdataintheelectroniccommercesystem,theneedforalargenumberofdatadepthanalysisisincreasinglyurgent.
Therefore,theuseofasimpleandhighscalabilityoftheprogramfortheanalysisofproductrecommendationisparticularlyimportant.
Atpresentdomesticmanyecommercesitesusecollaborativefilteringalgorithm,suchasAmazon,Dangdang,collaborativefilteringalgorithmismainlydividedintobasedontheitemsofthecollaborativefilteringalgorithmanduserbasedcollaborativefilteringalgorithm.
Basedonitemsofcollaborativefilteringalgorithmistomeasurethesimilaritybetweenitemsaccordingtotheuser'spreferences,donotneedtoconsidertheitemspecificcontentfeatures,sothealgorithmismainlyusedine-commercerecommendationandmovierecommendationdomain,thealgorithmwhileinthefieldofelectroniccommercerecommendationhasbeenacertaindegreeofsuccess.
Butinmassivedataarerecommendedwhenthedataisrecommendedperformanceisnothighandthedatainformationlackofsharingandextendedtheleadtothehardwarerequirementscomparedhigherinherentshortcomingsmakeitdidnotreceiveapromotionandsupportofenterpriseelectroniccommerce[2].
SoifweuseMapReducetoachievedistributedparallelcomputing,itwillgreatlyimprovetheefficiencyandperformanceofthealgorithm,andpromotethefurtherdevelopmentofthealgorithm[3-4].
Basedontheitemsofthecollaborativefilteringalgorithmisaccordingtoitemsimilarityanduserhistoryaccessrecordrecommendedtotheusertogeneratealistofitems,buttherearesomesmallproblems,suchasdatasparsityproblemandwhenthemassofusersandthenumberofitems,theuserbehaviorandrecorddatawillgreatly,andthealgorithmforcomputingitemswithsimilarmatrixcostgreatly,algorithmefficiencyandperformancewillgreatlyreduce.
Aimingattheaboveproblems,theclusteringalgorithmhasalsobeenappliedtoacollaborativefilteringalgorithmbasedonitem,themassiveuserclusteringanalysis,soitcanavoidthequestioncarefully,foreachusertorecommendoperation.
Thefirstshoppinguserswithsimilarinterestsintoauserclass,withaclusterofuserrecommendedgoodsarethesame.
Thesecondistoreducethemassiveuserdimensionsbecomedozensofclusteringlimited,thetimecomplexityencounteredabottleneck,andtheparallelclusteringalgorithmusingMapReduceistheeffectivewaytosolvethebottleneck[5].
MapReduceisadistributedprogrammingmodelframeworkonHadoopplatform,intheconditionofnotfamiliarwiththeunderlyingdetailsofthedistributedimplementationoftheimplementationoftheprogram[6].
TheMapReduceasparallelcomputingprogrammingmodel,firstofalltousersofMapReducebasedparallelclusteringandaccordingtotheresultsofuserclustering,ineveryuserclassusingtheMapReduceparallelcollaborativefilteringrecommendation,eventuallygiveusersareasonablepersonalizedcommodityrecommendationlist.
Therunningtimeofdifferentnodesinthequantitativedataiscomparedwiththenewalgorithm.
Theresultsshowthatthedataprocessingperformanceoftheproposedalgorithmisgreatlyimproved.
TheprincipleofMapReduceprogrammingmodelMapReduceisinHadoopplatformbyusingparallelcomputingprogrammingmodel,thetechniqueisproposedbyGoogleforatypicaldistributedparallelprogrammingmodel,theuserintheMapReducemodeldevelopthemapandreducefunctions,canrealizetheparallelprocessing.
Mapwillberesponsiblefordatadispersion,Reduceisresponsiblefordataaggregation.
UsersonlyneedtoachieveMapandReducetwointerface,youcancompletethecalculationofTBleveldata.
BecauseoftheMapReducemodel,thedetailsoftheparallelandfault-tolerantprocessingareencapsulated,whichmakesprogrammingveryeasytoimplement.
MapReduceparallelcalculationisdividedintotwoparts,thefirststepisinitializingtheoriginalinputdatafileandthedatasetisdividedintoapluralityofacertainsizeofdatablock,facilitateparallelcomputing;thesecondstepistostartthemapandreducefunctionsalgorithmofparallelcomputing,finallyproducedthefinalresult.
Figure1ParallelflowchartofMapReduceKeytechnologyresearchandImplementation1.
ThebasicideaofthetraditionalcollaborativefilteringalgorithmbasedonItem-basedThetraditionalbasedonitemsofcollaborativefilteringalgorithmthebasicideaisdividedintothreeparts,thefirstpartistocomputethesimilaritybetweenitems,commonsimilaritycalculationmethodwithcosinesimilarity,Pearsoncorrelationcoefficient,Tanmotocoefficientcorrelationof.
ThispaperselectstheEuclideansimilarityalgorithm,asfollows:TheassumptionisthatthereisavectorXandavectorY:X=(1x,2x,3x),Y=(1y,2y,3y),UsingtheEuclideansimilarityalgorithmtocalculatethesimilaritybetweenXandYSvector(x,y)formulaisasfollows[7]:1(,)1(,)Sxydxy=+(1)Where(,)dxyisthedistancebetweenthevectorXandY,thecalculationformulaisasfollows:222231123(dxyxyyyxx2)Thesecondpartistocalculatetheuserratingsmatrixontheitemsofthegoodsaccordingtothesimilaritymatrix;thethirdpartistheitemsimilaritymatrixWandtheusersoftheitemscorematrixmultiplicationtoobtaintherecommendationresults.
TraditionalItem-Basedcollaborativefilteringrecommendationalgorithmbasedonitemisthestagethataffectstheperformanceofthealgorithm.
Ifthenumberofusersisn,thenumberofcommodityitemsism,thetimecomplexityoffindingalltheitemsinthenprojectisO(2m),thetotalsearchspaceisnusers,sothetimecomplexityofcomputingsimilarityisO(2nm).
Sowhencalculatingthesimilaritymatrixofitems,itisindependentofthesimilaritybetweenthecalculatedandtheotherpairofitemstoaproject,soitispossibletocalculatethesimilaritymatrix.
2.
AnewalgorithmofItem-basedbasedonMapReduceThenewalgorithmismainlydividedintotwosteps;thefirststepistheMapReduceimplementationofK-Meansalgorithmbasedonclusteringofusers.
ThesecondstepistoachievetheparallelrecommendationalgorithmofItem-basedonMapReduce,theproductofuserclusteringrecommendation.
2.
1ThenewalgorithmK-MeansbasedonMapReduceThebasicideaofthetraditionalK-meansclusteringalgorithm:fromMdataobjectsinarbitrarychoiceofKobjectsastheinitialclustercenters;fortherestoftheotherobjects,accordingtotheirdistanceandtheclustercenters,respectively,theyallocatedtoitsmostsimilarclustering;thencalculateeachreceivedanewclusteringalgorithmclusteringcenter;keeprepeatingtheprocessuntilnochangesinacore.
Inthek-meansalgorithmtocalculatethedistancebetweendataobjectsandclustercentersisthemosttime-consumingoperation.
ThedataobjectandKclustercenterdistancecomparisonatthesametime,datafromotherobjectscanalsobecomparedwiththeKdistanceofthecenterofcluster,sotheoperationcanbeparallelized[8]BasedonMapReduceparallelimplementationofK-meansalgorithmcanimprovethespeedoftheclusteringalgorithm,isdividedintothreesteps:thefirststep:themapfunction,foreverypointcalculationrecentlythecenterdistanceandthecorrespondingtothenearestclustercenter.
Thesecondstep:Combinefunction,justcompletedtheMapmachineonthemachinearecompletedwiththesamepointoftheclusterpointofsummation,reducetheamountofcommunicationandcomputationofReduceoperation.
ThisstepisthekeytotheuseofCombinefunctiononthemachineonthefirstofthesameclustermerge,reducedtotheReducefunctionofthetransferandtheamountofcomputation.
Thethirdstep:theReducefunction,theintermediatedataofeachclustercenterwillbeformedandthenewclustercentercanbeobtained.
Eachiterationisrepeatedonthethreestep.
Figure2ParallelFlowChartofK-meansAlgorithmbasedonMapReduce2.
2thecollaborativefilteringalgorithmbasedonMapReduceforparallelimplementationofItem-basedBasedonthesimilaritycalculationformulamentionedabove(1),thispaperpresentsacollaborativefilteringrecommendationalgorithmbasedonMapReduce.
Algorithm1ThecollaborativefilteringrecommendationalgorithmbasedonMapReduceINPUT:Userinformationfile,Iteminformationfile,IntendeduserOUTPUT:IntendeduserrecommendedlistTheprocessisasfollows:Step1:Transformingtheuservectorintoanitemvector;Step2:Parallelcalculationofthesimilaritybetweenitems;thecalculationofthesimilaritybetweenitemsaccordingtotheformula(2)tocalculate;Step3:Similaritymatrixofparallelcomputingobjects;Step4:Parallelcomputinguserratingmatrix;inthecalculationoftheuser'sscoringmatrix,iftheuserisnotontheitemstoomuch,thenthedefaultscoreis1;Step5:Theresultsobtainedbythemultiplicationofthesimilaritymatrixofparallelcomputingobjectsandtheuser'sscorematrixarerecommended.
Experimentalresultanalysis1.
experimentalenvironmentThesimulationexperimentusingVMware_Workstation_10.
0.
3,virtualizationsoftwaretovirtualHadoopcloudplatform.
EightvirtualmachinesareinstalledonthevirtualHadoopcloudplatform,andaHadoopclusterenvironmentisbuiltontheseeightvirtualmachines.
OneofthevirtualmachineasagoodJobTrackernodeNameNode,theothersevenvirtualmachinesdeployedTaskTrackerandDataNode.
Thesemachinesareinthesamelocalareanetwork.
Theexperimentuseseightsetsofvirtualmachinehardwareconfigurationandsoftwareconfigurationasshownintable1:Table1HadoopClusterConfigurationOSCentos6.
4JDKVersion1.
6.
0Hadoop1.
1.
2HardWare2GRAM100GHardDisk2.
ExperimentandanalysisBasedonMapReduceparallelimplementationofItem-basedcollaborativefilteringalgorithminparallelmodeexpansionrateperformancecomparisontest,selectthesizeofthedataset,respectively,intheefficiencyof1-8nodesrunning.
Theexperimentalresultsareshownbelow:Figure3PerformanceTestChartFigure3isbasedonMapReduceparallelimplementationofitembasedcollaborativefilteringalgorithmcantestchart,theXaxisisthenumberofclients,they-axisistheresponsetimeofthesystem.
TheexperimentalresultsshowthatbasedonMapReduceparallelimplementationofitembasedcollaborativefilteringalgorithmperformancecomparedtothetraditionalrecommendationalgorithmissignificantlyimproved.
ConclusionInthispaper,anewalgorithmofcollaborativefilteringalgorithmbasedonMapReduceisproposed.
Theexperimentresultsshowthatthenewalgorithmhashighefficiencyandcanachievehighperformanceatalowcost.
Butinthispaper,theuserclusteringiscompletedonthebasisoftheuserwithasmallnumberofattributes,forhighdimensionalattributesoftheusergroups,butalsotodofurtherresearch.
Inadditiontothenewalgorithminthispaperhasbeenputforward,wewillcontinuetoimprovetheexperimentalmethod,andconstantlyimprovetheaccuracyoftherecommendationalgorithm.
References[1]Chenruming,Challenges,valuesandcopingstrategiesintheeraofbigdata[J].
MobileCommunications.
2012(17):14-15.
[2]SunLingfang,ZhangJing.
ElectronicrecommendationmechanismbasedonRFMmodelandcollaborativefiltering[J].
JournalofJiangsuUniversityofScienceandTechnology(NaturalScienceEdition).
2010,24(3):285-289.
[3]LIGai,PANRong.
etCollaborativefilteringalgorithmparallelizeresearchbasedonlargedatasetsa[J].
ComputerEngineeringandDesign,2012,33(6):2437-2441.
[4]LIWenhai;XUShuren;DesignandimplementationofrecommendationsystemforE-commerceonHadoop[J].
ComputerEngineeringandDesign,2014(35):131-136.
[5]SUNTianhao,LIAnnenget.
ResearchonDistributedCollaborativeFilteringRecommendationAlgorithmBasedonHadoop[J].
ComputerEngineeringandApplications,2014,51(15):124:128[6]XieXuelian,LiLanyou.
ResearchonParallelK-meansAlgorithmBasedonCloundComputingPlatform[J].
ComputerMeasurement&Control,2014,22(5):1510-1512.
[7]YanCun,JiGenlin.
DesignandImplementationofItem-BasedParallelCollaborativeFilteringAlgorithm[J].
JOURNALOFNANJINGNORMALUNIVERSITY(NaturalScienceEdition),2014,37(1):71-75.
[8]WAGNFei,QinXiaolin.
Algorithmfork-meansBasedonDataStreaminCloudComputing[J].
ComputerScience,2015,42(11):235:239.

10gbiz($2.36/月),香港/洛杉矶CN2 GIA线路VPS,香港/日本独立服务器

10gbiz发布了9月优惠方案,针对VPS、独立服务器、站群服务器、高防服务器等均提供了一系列优惠方面,其中香港/洛杉矶CN2 GIA线路VPS主机4折优惠继续,优惠后最低每月仅2.36美元起;日本/香港独立服务器提供特价款首月1.5折27.43美元起;站群/G口服务器首月半价,高防服务器永久8.5折等。这是一家成立于2020年的主机商,提供包括独立服务器租用和VPS主机等产品,数据中心包括美国洛...

HostKvm - 夏季云服务器七折优惠 香港和韩国机房月付5.95美元起

HostKvm,我们很多人都算是比较熟悉的国人服务商,旗下也有多个品牌,差异化多占位策略营销的,商家是一个创建于2013年的品牌,有提供中国香港、美国、日本、新加坡区域虚拟化服务器业务,所有业务均对中国大陆地区线路优化,已经如果做海外线路的话,竞争力不够。今天有看到HostKvm夏季优惠发布,主要针对香港国际和韩国VPS提供7折优惠,折后最低月付5.95美元,其他机房VPS依然是全场8折。第一、夏...

极光KVM美国美国洛杉矶元/极光kvmCN7月促销,美国CN2 GIA大带宽vps,洛杉矶联通CUVIP,14元/月起

极光KVM怎么样?极光KVM本月主打产品:美西CN2双向,1H1G100M,189/年!在美西CN2资源“一兆难求”的大环境下,CN2+大带宽 是很多用户的福音,也是商家实力的象征。目前,极光KVM在7月份的促销,7月促销,美国CN2 GIA大带宽vps,洛杉矶联通cuvip,14元/月起;香港CN2+BGP仅19元/月起,这次补货,机会,不要错过了。点击进入:极光KVM官方网站地址极光KVM七月...

centos6.0为你推荐
淘宝门户淘宝网怎么样从个人中心进入首页小度商城小度分期靠谱吗?netlife熊猫烧香图片22zizi.comwww 地址 didi22怎么打不开了,还有好看的吗>comwww.7160.com电影网站有那些百度指数词百度指数是指,词不管通过什么样的搜索引擎进行搜索,都会被算成百度指数吗?lcoc.toptop weenie 是什么?www.03024.comwww.sohu.com是什么鹤城勿扰齐齐哈尔,又叫鹤城吗?本冈一郎本冈一郎有副作用吗?主要有什么呢?
中文域名注册 vps教程 xenvps a5域名交易 国外服务器网站 gitcafe 免费smtp服务器 丹弗 40g硬盘 毫秒英文 河南移动邮件系统 免费个人空间 200g硬盘 cn3 香港新世界中心 怎么建立邮箱 web服务器搭建 空间租赁 移动服务器托管 hostease 更多