云計(jì)算時(shí)代的社交網(wǎng)絡(luò)平臺(tái)和技術(shù)Google-NanChang-Talk

云計(jì)算時(shí)代的社交網(wǎng)絡(luò)

平臺(tái)和技術(shù)

張智威副院長(zhǎng),研究院,谷歌中國(guó)教授,電機(jī)工程系,加州大學(xué)10/30/20231EdChang180million(↑25%)208million(↑

3%)60million(↑

90%)60million(↑

29%)500million180million600kEngineeringGraduatesMobilePhonesBroadbandUsersInternetPopulationChinaU.S.ChinaOpportunity

China&USin2006-0772k7200010/30/20232EdChangGoogleChinaSize(~700)200engineers400otheremployeesAlmost100internsLocationsBeijing(2005)Taipei(2006)Shanghai(2007)10/30/20233EdChangOrganizingtheWorld’sInformation,Socially社區(qū)平臺(tái)(SocialPlatform)云運(yùn)算(CloudComputing)結(jié)論與前瞻(ConcludingRemarks)10/30/20234EdChangWeb1.0.htm.htm.htm.jpg.jpg.doc.htm.msg.htm.htm10/30/20235EdChangWebwithPeople(2.0).htm.jpg.doc.xls.msg.htm.htm.jpg.msg.htm10/30/20236EdChang+SocialPlatforms.htm.jpg.doc.xls.msg.htm.htm.jpg.msg.htmApp(Gadget)App(Gadget)10/30/20237EdChang10/30/20238EdChang10/30/20239EdChang10/30/202310EdChang10/30/202311EdChang開(kāi)放社區(qū)平臺(tái)10/30/202312EdChang10/30/202313EdChang10/30/202314EdChang10/30/202315EdChang10/30/202316EdChang開(kāi)放社區(qū)平臺(tái)社區(qū)平臺(tái)1我是誰(shuí)2我的朋友3他的活動(dòng)10/30/202317EdChang10/30/202318EdChang10/30/202319EdChang開(kāi)放社區(qū)平臺(tái)社區(qū)平臺(tái)1我是誰(shuí)2我的朋友3他的活動(dòng)4他的東西10/30/202320EdChang10/30/202321EdChangSocialGraph10/30/202322EdChang10/30/202323EdChangWhatUsersWant?PeoplecareaboutotherpeoplecareaboutpeopletheyknowconnecttopeopletheydonotknowDiscoverinterestinginformationbasedonotherpeopleaboutwhootherpeopleareaboutwhatotherpeoplearedoing10/30/202324EdChangInformationOverflowChallengeToomanypeople,toomanychoicesofforumsandapps“Isoonneedtohireafull-timetomanagemyonlinesocialnetworks”DesiringaSocialNetworkRecommendationSystem10/30/202325EdChangRecommendationSystemFriendRecommendationCommunity/ForumRecommendationApplicationSuggestionAdsMatching10/30/202326EdChangOrganizingtheWorld’sInformation,Socially社區(qū)平臺(tái)(SocialPlatform)云運(yùn)算(CloudComputing)結(jié)論與前瞻(ConcludingRemarks)10/30/202327EdChangpicturesource:http://

（1）數(shù)據(jù)在云端不怕丟失不必備份（2）軟件在云端不必下載自動(dòng)升級(jí)（3）無(wú)所不在的云計(jì)算任何設(shè)備登錄后就是你的（4）無(wú)限強(qiáng)大的云計(jì)算無(wú)限空間無(wú)限速度業(yè)界趨勢(shì)：云計(jì)算時(shí)代的到來(lái)10/30/202328EdChang互聯(lián)網(wǎng)搜索：云計(jì)算的例子1.用戶輸入查詢關(guān)鍵字CloudComputing2.分布式預(yù)處理數(shù)據(jù)以便為搜索提供服務(wù):GoogleInfrastructure(thousandsofcommodityserversaroundtheworld)MapReduceformassdataprocessingGoogleFileSystem3.返回搜索結(jié)果10/30/202329EdChangGivenamatrixthat“encodes”dataCollaborativeFiltering10/30/202330EdChangGivenamatrixthat“encodes”dataManyapplications(collaborativefiltering):

User

–

CommunityUser–UserAds–UserAds–Communityetc.UsersCommunities10/30/202331EdChangCollaborativeFiltering(CF)

[Breese,HeckermanandKadie1998]Memory-basedGivenuseru,find“similar”users(knearestneighbors)Boughtsimilaritems,sawsimilarmovies,similarprofiles,etc.DifferentsimilaritymeasuresyielddifferenttechniquesMakepredictionsbasedonthepreferencesofthese“similar”usersModel-basedBuildamodelofrelationshipbetweensubjectmattersMakepredictionsbasedontheconstructedmodel10/30/202332EdChangMemory-BasedModel

[Goldbertetal.1992;Resniketal.1994;Konstantetal.1997]

ProsSimplicity,avoidmodel-buildingstageConsMemoryandTimeconsuming,usestheentiredatabaseeverytimetomakeapredictionCannotmakepredictioniftheuserhasnoitemsincommonwithotherusers10/30/202333EdChangModel-BasedModel

[Breeseetal.1998;Hoffman1999;Bleietal.2004]

ProsScalability,modelismuchsmallerthantheactualdatasetFasterprediction,querythemodelinsteadoftheentiredatasetConsModel-buildingtakestime10/30/202334EdChangAlgorithmSelectionCriteriaNear-real-timeRecommendationScalableTrainingIncrementalTrainingisDesirableCandealwithdatascarcityCloudComputing!10/30/202335EdChangModel-basedPriorWorkLatentSemanticAnalysis(LSA)ProbabilisticLSA(PLSA)LatentDirichletAllocation(LDA)10/30/202336EdChangLatentSemanticAnalysis(LSA)

[Deerwesteretal.1990]Maphigh-dimensionalcountvectorstolowerdimensionalrepresentationcalledlatentsemanticspaceBySVDdecomposition:A=U∑VTA=Word-documentco-occurrencematrixUij

=Howlikelywordibelongstotopicj∑jj

=HowsignificanttopicjisVijT=HowlikelytopicibelongstodocjWordsDocsTopicsDocsTopicsTopicsTopicsWordsWxDWxTTxTTxD10/30/202337EdChangLatentSemanticAnalysis(cont.)LSAkeepsk-largestsingularvaluesLow-rankapproximationtotheoriginalmatrixSavespace,de-noisifiedandreducesparsityMakerecommendationsusing?Word-wordsimilarity:??TDoc-docsimilarity:?T

?Word-docrelationship:?WordsDocsTopicsDocsTopicsTopicsTopicsWordsWxDWxKKxKKxD????10/30/202338EdChangProbabilisticLatentSemanticAnalysis(PLSA)[Hoffman1999;Hoffman2004]DocumentisviewedasabagofwordsAlatentsemanticlayerisconstructedinbetweendocumentsandwordsP(w,d)=P(d)P(w|d)=P(d)∑zP(w|z)P(z|d)ProbabilitydeliversexplicitmeaningP(w|w),P(d|d),P(d,w)ModellearningviaEMalgorithmP(d)dwzP(z|d)P(w|z)10/30/202339EdChangPLSAextensionsPHITS[Cohn&Chang2000]Modeldocument-citationco-occurrenceAlinearcombinationofPLSAandPHITS[Cohn&Hoffmann2001]Modelcontents(words)andinter-connectivityofdocumentsLDA[Bleietal.2003]ProvideacompletegenerativemodelwithDirichletpriorAT[Griffiths&Steyvers2004]IncludeauthorshipinformationDocumentiscategorizedbyauthorsandtopicsART[McCallum2004]IncludeemailrecipientasadditionalinformationEmailiscategorizedbyauthor,recipientsandtopics10/30/202340EdChangCombinationalCollaborativeFiltering(CCF)FusemultipleinformationAlleviatetheinformationsparsityproblemHybridtrainingschemeGibbssamplingasinitializationsforEMalgorithmParallelizationAchievelinearspeedupwiththenumberofmachines10/30/202341EdChangNotationsGivenacollectionofco-occurrencedataCommunity:C={c1,c2,…,cN}User:U={u1,u2,…,uM}Description:D={d1,d2,…,dV}Latentaspect:Z={z1,z2,…,zK}ModelsBaselinemodelsCommunity-User(C-U)modelCommunity-Description(C-D)modelCCF:CombinationalCollaborativeFilteringCombinesbothbaselinemodels10/30/202342EdChangBaselineModelsCommunity-User(C-U)modelCommunity-Description(C-D)modelCommunityisviewedasabagofusers

canduarerenderedconditionallyindependentbyintroducingzGenerativeprocess,foreachuseru1.Acommunitycischosenuniformly2.AtopiczisselectedfromP(z|c)3.AuseruisgeneratedfromP(u|z)Communityisviewedasabagofwords

canddarerenderedconditionallyindependentbyintroducingzGenerativeprocess,foreachwordd1.Acommunitycischosenuniformly2.AtopiczisselectedfromP(z|c)3.AworddisgeneratedfromP(d|z)10/30/202343EdChangBaselineModels(cont.)Community-User(C-U)modelCommunity-Description(C-D)model

Pros1.Personalizedcommunitysuggestion

Cons

1.C-Umatrixissparse,maysufferfrom

informationsparsityproblem2.Cannottakeadvantageofcontent

similaritybetweencommunities

Pros1.Clustercommunitiesbasedoncommunitycontent(descriptionwords)

Cons

1.Nopersonalizedrecommendation2.Donotconsidertheoverlappedusersbetweencommunities10/30/202344EdChangCCFModelCombinationalCollaborativeFiltering(CCF)modelCCFcombinesbothbaselinemodelsAcommunityisviewedas

-abagofusersANDabagofwordsByaddingC-U,CCFcanperformpersonalizedrecommendationwhichC-Dalonecannot

ByaddingC-D,CCFcanperformbetterpersonalizedrecommendationthanC-UalonewhichmaysufferfromsparsityThingsCCFcandothatC-UandC-Dcannot-P(d|u),relateusertoword-Usefulforusertargetingads10/30/202345EdChangAlgorithmRequirementsNear-real-timeRecommendationScalableTrainingIncrementalTrainingisDesirable10/30/202346EdChangParallelizingCCFDetailsomitted10/30/202347EdChangpicturesource:http://

（1）數(shù)據(jù)在云端不怕丟失不必備份（2）軟件在云端不必下載自動(dòng)升級(jí)（3）無(wú)所不在的云計(jì)算任何設(shè)備登錄后就是你的（4）無(wú)限強(qiáng)大的云計(jì)算無(wú)限空間無(wú)限速度業(yè)界趨勢(shì)：云計(jì)算時(shí)代的到來(lái)10/30/202348EdChangExperimentsonOrkutDatasetDatadescriptionCollectedonJuly26,2007TwotypesofdatawereextractedCommunity-user,community-description312,385users109,987communities191,034uniqueEnglishwordsCommunityrecommendationCommunitysimilarity/clusteringUsersimilaritySpeedup10/30/202349EdChangCommunityRecommendationEvaluationMethodNoground-truth,nouserclicksavailableLeave-one-out:randomlydeleteonecommunityforeachuserWhetherthedeletedcommunitycanberecoveredEvaluationmetricPrecisionandRecall10/30/202350EdChangResultsObservations:

CCFoutperformsC-UFortop20,precision/recallofCCF

aretwicehigherthanthoseofC-U

Themorecommunitiesauserhasjoined,thebetterCCF/C-Ucanpredict10/30/202351EdChangRuntimeSpeedupTheOrkutdatasetenjoysalinearspeedupwhenthenumberofmachinesisupto100Reducesthetrainingtimefromonedaytolessthan14minutesBut,whatmakesthespeedupslowdownafter100machines?10/30/202352EdChangRuntimeSpeedup(cont.)Trainingtimeconsistsoftwoparts:Computationtime(Comp)Communicationtime(Comm)10/30/202353EdChangCCFSummaryCombinationalCollaborativeFilteringFusebagsofwordsandbagsofusersinformationHybridtrainingprovidesbetterinitializationsforEMratherthanrandomseedingParallelizetohandlelarge-scaledatasets10/30/202354EdChangChina’sContributionson/to

CloudComputingParallelCCFParallelSVMs(KernelMachines)ParallelSVDParallelSpectralClusteringParallelExpectationMaximizationParallelAssociationMiningParallelLDA

10/30/202355EdChangSpeedingupSVMs

[NIPS2007]ApproximateMatrixFactorizationParallelizationOpensource@/p/psvm350+downloadssinceDecember07Ataskthattakes7dayson1

machinetakes1hourson500machines10/30/202356EdChangIncompleteCholeskyFactorization(ICF)nxnnxppxnp<<nConserveStorage10/30/202357EdChangMatrixProduct=pxnnxppxp10/30/202358EdChangOrganizingtheWorld’sInformation,Socially社區(qū)平臺(tái)(SocialPlatform)云運(yùn)算(CloudComputing)結(jié)論與前瞻(ConcludingRemarks)10/30/202359EdChangWebWithPeople.htm.htm.htm.jpg.jpg.doc.xls.msg.msg.htm10/30/202360EdChangWhatNextforWebSearch?

PersonalizationReturnqueryresultsconsideringpersonalpreferencesExample:DisambiguatesynonymlikefujiOops:severaltried,theproblemishardTrainingdatadifficulttocollectenough(forcollaborativefiltering)Computationalintensivetosupportpersonalization(e.g.,forpersonalizingpagerank)Userprofilemaybeincomplete,erroneous10/30/202361EdChang個(gè)人搜索智能搜索搜索“富士”可返回富士山富士蘋果富士相機(jī)10/30/202362EdChang10/30/202363EdChang10/30/202364EdChang10/30/202365EdChang10/30/202366EdChangOrganizingWorld’sInformation,SociallyWebisaCollectionofDocumentsandPeopleRecommendationisaPersonalized,PushModelofSearchCollaborativeFilteringRequiresDenseInformationtobeEffectiveCloudComputingisEssential10/30/202367EdChangReferences[1]Alexainternet.http:///.[2]D.M.BleiandM.I.Jordan.Variationalmethodsforthe

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