0-云计算时代的社交网络-平台和技术-张智威

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术张智威副院长，研究院，谷歌中国教授，电机工程系，加州大学2/16/2011

Ed

Chang

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intesityonrweouanesvncEd

Chang

23Darkeshade2/16/2011T

sri

:

o

M1

$i

suteominnt1:*ssimUWhat

UsersWant?·People

care

about

other

people一careabout

peoplethey

know·

一

connect

to

people

they

do

not

know一

about

who

other

people

are一aboutwhatotherpeople

are

doingDiscover

interesting

information一

basedonother

people2/16/2011

Ed

Chang

24InformationOverflow

ChallengeT

u

n

n

,too

many

choices

of·DesiringaSocialNetworkRecommendation

System2/16/2011

Ed

Chang

25appseopleaysammforoo··“/

ds

i

e

me

to

manageorkfulltwanreliaheenlinneonmysooRecommendationSystemu

n

mmendationoncoRdaForumommeyenitRComFrien···Application

Suggestion

·Ads

Matching2/16/2011

Ed

Chang

26Organizing

the

World's

Information,Socially·

社

区平

台(Social

Platform)·云

运

算(Cloud

Computing)·结论与前瞻(Concluding

Remarks)2/16/2011Ed

Chang27(3

)

算(4

的云计算空强无限无限··)是你的的云计就备在后设不录何所登任无··(1)数据在云端·不怕丢失·不必备份(2

)

端升下级载在云动必件不自软··业界趋势：云计算时代的到来无限速度互联网搜索：

云计算的例子2.分布式预处理数据以便为搜索提供服务：

Gcogle

Infrastructure(thousands

sesdi

e

for

mass

data一

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FileSystem一ngucproMa—taVeteateTatH* *

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msE+eaooglIn

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arcund

theworldofco1.用户输入查询关键字3.返回搜索结果2/16/2011netEelermCemuoudtse2145541335245341352141554254331521312345133352115241355125Collaborative

FilteringGiven

a

matrix

that“encodes”data2/16/2011

Ed

Chang

30214554?133524?53?413521?455425?2

4335213123451333?52?1152?4435451245?Given

a

matrix

that“encodes

”dataManyapplications·User-Community·User-User·Ads

-User·Ads-Community·etc.(collaborativefiltering):Ed

Chang

31Communities2/16/2011UsersCollaborative

Filtering(CF)[Breese,Heckerman

and

Kadie

1998]·Memory-based—

h

il

fi

sm

i

r

,

)·Model-based—Build

a

model

of

relationship

between

subject

matters一Make

predictions

basedonthe

constructedmodelcstreolehprofineiglasts,simieareenrrilasemuwsiilar”samssr,aumrsisetugnBouiveDifferent

similarity

measures

yield

different

techniques一

ons

based

on

the

preferences

of

theseersictiueimilarake

p“sM2/16/2011

Ed

Chang

32Memory-Based

Model[Goldbertetal.1992;Resniket

al.1994;Konstant

et

al.1997]·

Pros一

Simplicity,avoid

model-building

stage·

Cons—MemoryandTimeconsuming,uses

the

entiredatabaseeverytimetomake

aprediction一

Cannotmake

prediction

ifthe

user

has

no

items

incommonwithother

users2/16/2011

Ed

Chang

33Model-Based

Model[Breese

et

al.1998;Hoffman

1999;Blei

et

al.2004]Pros一

it

t

is

much

smaller

than

the

一

nti

ery

the

model

instead

ofCons一

Model-buildingtakestimedatasetdiction,quererpeertheFastatasemodelllabiactuScala2/16/2011

Ed

Chang

34Algorithm

Selection

Criteria

S

al

i

commendation·CloudComputing!ngReainmeTr-ticalableNear-re·

e

t

it

r

i

gs

r

irablecityDesasciantianhTwaldealmenCanIncr2/16/2011

Ed

Chang

35Model-based

PriorWorkLatent

Semantic

Analysis

(LSA)·

ProbabilisticLSA(PLSA)··Latent

Dirichlet

Allocation(LDA)2/16/2011

Ed

Chang

36·

Maphigh-dimensional

count

vectors

tolowerdimensional

representation

called

latent

semantic

space·BySVD

decomposition:A=UEVTDocs

Word×D

SWxD

WxTA=Word-document

co-occurrence

matrixU;

=How

likely

word

i

belongs

to

topic

jji

=How

significant

topic

j

isVi¹=How

likely

topic

i

belongs

to

docjLatent

Semantic

Analysis(LSA)[Deerwester

et

al.1990]2/16/2011

Ed

Chang

37

Latent

Semantic

Analysis(cont.)·LSAkeepsk-largestsingular

values一

Low-rankapproximationtothe

original

matrix一

Savespace,de-noisifiedandreducesparsityOCS·Make

ecommendations

usingA—Word-word

similarity:A

AT-Doc-doc

similarity:ÄT

A—Word-doc

relationship:AWxKWxrDATopKxD2/16/2011ChangWordsEd38Probabilistic

Latent

Semantic

Analysis(PLSA)[Hoffman

1999;Hoffman20041Document

is

viewed

as

a

bag

ofwords··

|

,

licit

meaningiEMexpviwrsPlingd),deea,Plw)ityodel-P(robaMP··A

latent

semantic

layer

isconstructed

inbetweendocuments

and

words

·P(w,d)=P(d)P(w|d)=P(d)EzP(w|z)P(z|d)algorithm2/16/2011ChangEd39·

LDA[Blei

et

al.2003]一

Provideacompletegenerativemodelwith

Dirichlet

prior

·

AT

[Griffiths

&Steyvers

2004]一

Includeauthorshipinformation一

Document

iscategorizedbyauthors

andtopics·

ART[McCallum2004]一

Includeemailrecipientas

additional

information一

Email

is

categorized

by

author,recipients

andtopics2/16/2011

Ed

Chang

40PLSAextensions·

PHITS[Cohn

&Chang

2000]·

e

t

u

[Cohn

&Hoffmann

2001]一

Model

contents(words)and

inter-connectivity

of

documentsHITSencePrrSA

andco-occLnPiombinationofocument-citaclA

li一CombinationalCollaborativeFiltering(CCF)·Fusemultiple

information—Alleviate

the

information

sparsity

problem·Hybridtrainingscheme—Gibbs

sampling

as

initializations

for

EM·Parallelization—Achieve

linear

speedup

with

the

numberof

machines2/16/2011

Ed

Chang

41algorithm·

Givenacollectionofco-occurrencedata-Community:C

={C₁

,C₂

,…,C}-User:U={u₁

,u₂

,…,um}一Description:D={d₁

,d₂

,…,dv}-Latentaspect:Z={z₁

,z₂

,…,zk}·Models—Baseline

models·Community-User(C-U)model·Community-Description(C-D)model-CCF:CombinationalCollaborativeFiltering·

Combines

both

baseline

modelsNotations2/16/2011ChangEd42·Communityis

viewed

as

a

bag

ofwords

·canddarerendered

conditionally·Gi

ent

pr

rodu

zword

d1.A

community

cischosenuniformly

2.A

topic

zisselectedfromP(z|c)3.Awordd

isgenerated

from

P(d|z)Chang

43hgcnaiecrtonss,firativpeneeen·Communityisviewed

as

a

bag

of

usersc

and

u

are

rendered

conditionallyindependentbyintroducingz■(

Generative

process,for

each

user

u

1.Acommunitycischosenuniformly

2.A

topic

zisselected

fromP(z|c)3.Auser

u

is

generatedfrom

P(u|z)2/16/2011

EdModelsCommunity-Description(C-D)model

BaselineCommunity-User(C-U)model

-Pros1.Cluster

communities

based

oncommunity

content(description

words)-Cons1.No

personalized

recommendation2.Donot

considerthe

overlapped

usersbetween

communitiesChang

441.C-U

matrix

information2.Cannot

take

similarity2/16/2011is

sparse,may

sufferfromsparsity

problemadvantage

of

contentbetween

communitiesEdModels(cont.)Community-Description(C-D)model

BaselineCommunity-User(C-U)model

-Pros1.Personalized*Conssuggestioncommunity·CCFcombines

both

baseline

models*A

community

isviewed

as-abag

of

users

AND

a

bag

ofwords*By

adding

C-U,CCF

can

performpersonalizedrecommendationwhichC-Dalone

cannot·By

adding

C-D,CCF

can

perform

betterpersonalizedrecommendationthan

C-Ualonewhich

may

sufferfrom

sparsity·Things

CCF

can

do

that

C-U

and

C-Dcannot-P(d)u),relate

user

to

word-Useful

for

user

targeting

adsCombinational

Filtering(CCF)model

C

P(c)—P(zlc)ZP(ulz)

P(dlz)U

dModelCCF2/16/2011CollaborativeChangEd45Algorithm

Requirements

S

al

i

commendationngReainmeTr-ticalableNear-reIncrementalTraining

is

Desirable2/16/2011

Ed

Chang

46ParallelizingCCFDetailsomitted2/16/2011

Ed

Chang

47(3

)

算(4

的云计算空强无限无限··)是你的的云计就备在后设不录何所登任无··(1)数据在云端·不怕丢失·不必备份(2

)

端升下级载在云动必件不自软··业界趋势：云计算时代的到来无限速度ExperimentsonOrkut

Dataset·Data

description-Collected

on一

Two

types

ofJuly

26,2007data

were

extracted·Community-user,community-description

一312,385users—109,987communities·

—191,034

unique

Englishwords·Speedup·

Community

recommendation·U

m

i

milarity/clusteringtysiartylisimimunserCo2/16/2011Ed

Chang49Community

Recommendation·

Evaluation

Method一

No

ground-truth,no

user

clicks

available—Leave-one-out:randomly

delete

one

community

foreach

user一

Whether

the

deleted

community

can

be

recovered·

Evaluation

metric—Precisionand

Recall2/16/2011

Ed

Chang

50Lengthoftherecommendation

listPecertageObservations:

CCFoutperforms

C-U

e

cboe

u

Citi

C

Uu

nhascaserttermmeehr,todmjoinThe口

For

top20,precision/recall

of

CCFare

twice

higher

than

those

of

C-UNumber

of

communities

a

user

has

joinedEpredict2/16/2011Ed

Chang51·

The

Orkut

dataset

enjoys

a

linear

speedup

when

the

number

of2/16/2011

Ed

Chang

52machines

is

up

to

100

Reduces

the

training

time

from

one

day

to

less

than

14

minutes··RuntimeSpeedupMachinesTime(aee.)Specdup100.23310204,32621.3502.28040.510O1,01491.1200706116But,what

makes

the

speedup

slow

down

after

100

machines?Number

of

mnchinesSpeedup200RuntimeSpeedup(cont.)·Trainingtimeconsistsoftwo

parts:一

Computationtime(Comp)一

Communicationtime(Comm)sdoedupNumberofmachinesNumbarofmachines2/16/2011

Ed

Chang

53CCFSummary·CombinationalCollaborative

Filtering—Fuse

bags

ofwordsand

bags

of

usersinformation—Hybridtrainingprovides

better

adliitionsfor

EM

ratherthan

random一

Parallelizeto

handle

large-scaledatasetsngzaetiseini2/16/2011

Ed

Chang

54China'sContributionson/to

CloudComputing

Parallel

CCF Parallel

SVMs(Kernel

Machines)·

ParallelSpectral

Clustering·

Parallel

Expectation

Maximization

·ParallelAssociation

Mining·

Parallel

LDA2/16/2011

Ed

Chang

55Parallel

SVDSpeeding

up

SVMs

[NIPS

2007]·Approximate

MatrixFactorization·

Parallelization

Open

source

@/p/psvm·A

task

that

takes

7

dayson

1machinetakes

1

hourson500

machines350+downloads

since

December

072/16/2011

Ed

Chang

56≈XIncompleteCholesky

Factorization(ICF)p<<n→Conserve

Storage2/16/2011

Ed

Chang