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.
profitserver正在对德国vps(法兰克福)、西班牙vps(马德里)、荷兰vps(杜廷赫姆)这3处数据中心内的VPS进行5折优惠促销。所有VPS基于KVM虚拟,纯SSD阵列,自带一个IPv4,不限制流量,在后台支持自定义ISO文件,方便大家折腾!此外还有以下数据中心:俄罗斯(多机房)、捷克、保加利亚、立陶宛、新加坡、美国(洛杉矶、锡考克斯、迈阿密)、瑞士、波兰、乌克兰,VPS和前面的一样性...
profitserver怎么样?profitserver是一家成立于2003的主机商家,是ITC控股的一个部门,主要经营的产品域名、SSL证书、虚拟主机、VPS和独立服务器,机房有俄罗斯、新加坡、荷兰、美国、保加利亚,VPS采用的是KVM虚拟架构,硬盘采用纯SSD,而且最大的优势是不限制流量,大公司运营,机器比较稳定,数据中心众多。此次ProfitServer正在对德国VPS(法兰克福)、西班牙v...
UCloud优刻得商家这几年应该已经被我们不少的个人站长用户认知,且确实在当下阿里云、腾讯云服务商不断的只促销服务于新用户活动,给我们很多老用户折扣的空间不多。于是,我们可以通过拓展选择其他同类服务商享受新人的福利,这里其中之一就选择UCloud商家。UCloud服务商2020年创业板上市的,实际上很早就有认识到,那时候价格高的离谱,谁让他们只服务有钱的企业用户呢。这里希望融入到我们大众消费者,你...
centos6.0为你推荐
硬盘工作原理数据存储的原理是什么rawtools佳能单反照相机的RAW、5.0M 是什么意思?8090lu.com8090向前冲电影 8090向前冲清晰版 8090向前冲在线观看 8090向前冲播放 8090向前冲视频下载地址??javbibitreebibi是什么牌子的www.kaspersky.com.cn卡巴斯基中国总部设立在?ww.66bobo.com谁知道11qqq com被换成哪个网站www.15job.com南方人才市场有官方网站是什么?www.1diaocha.com请问网络上可以做兼职赚钱吗?现在骗子比较多,不敢盲目相信。请大家推荐下www.147.qqq.com谁有147清晰的视频?学习学习月风随笔关于春夏秋冬的散文
免费linux主机 132邮箱 云网数据 hostmaster 服务器怎么绑定域名 本网站服务器在美国 京东商城0元抢购 193邮箱 免费mysql数据库 湖南idc 美国vpn代理 godaddy中文 e-mail 留言板 瓦工技术 ddos攻击器下载 中国最年轻博士 qq空间登入 灵动鬼影实录2 彩虹云点播官网 更多