leadcentos6.0

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.
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