HowComputersKnowWhatWeWant BeforeWeDoWord格式.docx

HowComputersKnowWhatWeWant BeforeWeDoWord格式.docx

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veamassedadatabasecontainingdetailedprofilesof740,000differentsongs.WestergrencallsthisdatabasetheMusicGenomeProject.

Thereisapointtoallthis,apartfromsettlingbarbetsaboutwhichsonghasthemostprominentbanjopartever.ThepurposeoftheMusicGenomeProjectistomakepredictionsaboutwhatkindofmusicyou'

regoingtolikenext.PandorausestheMusicGenomeProjecttopowerwhat'

sknowninthebusinessasarecommendationengine:

oneofthosepiecesofsoftwarethatgivesyouadviceaboutwhatyoumightenjoylisteningtoorwatchingorreadingnext,basedonwhatyoujustlistenedtoorwatchedorread.TellPandorayoulikeSpoonandi'

tllplayyouModestMouse.TellityoulikeCajunaccordionvirtuosoAlphonse“BoisSec”Ardoinandit'

lltryyououtonsomeIryLeJeune.EnoughpeopleliketellingPandorawhattheylikethattheserviceadds2.5millionnewusersamonth.

Overthepastdecade,recommendationengineshavebecomequietlyubiquitous.Attheappropriatemoment-generallywhenyou'

reabouttoconsummatearetailpurchase-theyappearatyourshoulder,whisperingsuggestivelyinyourear.Amazonwasthepioneerofautomatedrecommendations,butNetflix,Apple,YouTubeandTiVohavethemtoo.Inthemusicspacealone,Pandorahasdozensofcompetitors.Agoodrecommendationengineisworthalotofmoney.AccordingtoareportbyindustryanalystForrester,one-thirdofcustomerswhonoticerecommendationsonane-commercesitewindupbuyingsomethingbasedonthem.

Thetroublewithrecommendationenginesisthatthey'

rereallyhardtobuild.Theylooksimpleontheoutside-ifyoulikedX,you'

llloveY!

-butthey'

reactuallydoingsomethingfiendishlycomplex.They'

reprocessingastoundingquantitiesofdataanddoingsowithseriouslyhigh-levelmath.That'

sbecausethey'

reattemptingtosecond-guessamysterious,perverseandprofoundlyhumanformofbehavior:

thepersonalresponsetoaworkofart.They'

retryingtoreverse-engineerthesoul.They'

realsochangingthewayourcultureworks.Weusedtolearnaboutnewworksofartfromfriendsandcriticsandvideo-storeclerks-frompeople,inotherwords.Nowwelearnaboutthemfromsoftware.There'

sanewclassoftastemakers,andthe'

yrenothuman.LearningtoLoveDolphLundgren

Pandoramakesrecommendationsthesamewaypeopledo,moreorless:

byknowingsomethingaboutthemusicit'

srecommendingandsomethingaboutyourmusicaltaste.Butthat'

sactuallyprettyunusual.It'

saverylabor-intensiveapproach.Mostrecommendationenginesworkbackwardinstead,usinginformationthatcomesnotfromtheartbutfromitsaudience.

It'

satechniquecalledcollaborativefiltering,anditworksontheprinciplethatthebehaviorofalotofpeoplecanbeusedtomakeeducatedguessesaboutthebehaviorofasingleindividual.Her'

estheidea:

if,statisticallyspeaking,mostpeoplewholikedthefirstSexandtheCitymoviealsolikeMammaMia!

thenifweknowthataparticularindividuallikedSexandtheCity,wecanmakeaneducatedguessthatthatindividualwillalsolikeMammaMia!

Itsoundssimpleenough,butthecloseryoulook,theweirderandmorecomplicateditgets.TakeNetflix'

srecommendationengine,whichithasdubbedCinematch.Thealgorithmicgutsofarecommendationengineareusuallyafiercelyguardedtradesecret,butin2006Netflixdecideditwasn'

tcompletelyhappywithCinematch,andittookanunusualapproachtosolvingtheproblem.Thecompanymadepublicaportionofitsdatabaseofmovieratings-around100millionofthem-andofferedaprizeof$1milliontoanybodywhocouldimproveitsengineby10%.TheNetflixcompetitionopenedawindowontoaworldthat'

susuallylockedawaydeepinthebowelsofcorporateR&

Ddepartments.Theeventualwinner-whichclinchedtheprizelastfall-wasaseven-man,four-countryconsortiumcalledBellKor'

sPragmaticChaos,whichincludedBobBellandChrisVolinsky,twomembersofAT&

'

Tsresearchdivision.Talkingtothem,youstarttoseehowdifficultitistomakeapieceofsoftwareunderstandthevagariesofhumantaste.Youalsoseehow,oddly,softwareunderstandsthingsaboutourtasteinmoviesthatahumanvideoclerknevercould.

Thekeypointtograspaboutcollaborative-filteringsoftwareisthatitknowsabsolutelynothingaboutmovies.Ithasnopreconceptions;

itworksentirelyonthebasisoftheaudience'

sreaction.Soifalargeenoughgroupofpeopleclaimtohaveenjoyed,say,bothSawVandOnGoldenPond,thesoftwarewouldbeforcedtoinferthatthosetwomoviessharesomecommonqualitythattheviewersenjoyed.Crazy?

Orcrazygenius?

Insuchacase,thesoftwarewouldhavediscoveredanaestheticpropertythatwemightnotevenbeawareoforhaveanameforbutwhichinamathematicalsensemustbesaidtoexist.EvenBellandVolinskydon'

talwaysknowwhatthepropertiesare.“Wemightbeabletodescribethem,orwemightnotbeableto,”Bellsays.“Theymightbesubtletieslike‘actionmoviesthatdon'

thavealotofblood,don'

thavealotofprofanitybuthaveastrongfemalelead.'

Thingslikethat,whichyouwouldneverthinktocategorizeonyourown.”AsVolinskyputsit,“Alotoftimes,wedon'

tcomeupwithexplanationsthatareexplainable.”

Thatmakesrecommendationenginessoundpracticallypsychic,buteverydayexperiencetellsusthatthey'

reactuallyprettyfallible.EverybodyhasfelttheoutragethatcomeswhenarecommendationengineaccusesoneofasecretdesiretowatchRockyIV,theonewithDolphLundgreninit.In2006,WalmartwaschargedwithracismwhenitsrecommendationenginepairedPlanetoftheApeswithadocumentaryaboutMartinLutherKing.Butgenerallyspeaking,theweaklinkinarecommendationengineisn'

tthesoftware;

it'

sus.Collaborativefilteringworksonlyaswellasthedataithasavailable,andhumansproducenoisy,low-qualitydata.

Theproblemisconsistency:

we'

rejustnotgoodatexpressingourdesiresinratingform.Weratethingsdifferentlyafterabaddayatworkthanwewouldifwewereonvacation.Somepeoplearenaturallystingywiththeirstars;

othersaregenerous.Weratemoviesdifferentlydependingonwhetherweratethemrightafterwatchingthemorifwewaitaweek,anddifferentlyagaindependingonwhetherwesawalousymovieoragoodmovieinthatinterveningweek.Weevenratedifferentlydependingonwhetherwerateawholebatchofmoviestogetheroroneatatime.

Allthismeansthatthere'

saceilingtohowaccuratecollaborativefilteringcanget.“There'

salotofrandomnessinvolved,”Volinskyadmits.“There'

ssomeintrinsicleveloferrorassociatedwithtryingtopredicthumanbehavior.”

TheGreatChoiceEpidemic

Recommendationenginesarearesponsetothestrangenewworldofonlineretail.It'

saworldcharacterizedbyasurplusofsomethingweusuallycan'

tgetenoughof:

choice.

We'

redrowninginit.AsSheenaIyengarpointsoutinherbookTheArtofChoosing,in1994therewere500,000differentconsumergoodsforsaleintheU.S.NowAmazonaloneoffers24million.Whenfacedwithsuchanoversupplyofchoice,ourlittlelizardbrainsgostraighttovaporlock.“Wethinktheprofusionofpossibilitiesmustmakeitthatmucheasiertofindthatperfectgiftforafriend'

sbirthday,”Iyengarwrites,“onlytofindourselvesparalyzedinthefaceofrowuponrowofpotentialpresents.”We'

relivingthroughanepidemicofchoice.Werequireaninformationalprosthesistonavigateit.Therecommendationengineisthatprosthesis:

itwinnowsthemillionsofoptionsdowntoamanageablehandful.

Butthere'

satrade-offinvolved.Recommendationenginesintroduceanewvoiceintotheculturalconversation,onethatspeakstouswhenwe'

reatourmostvulnerable,whichistosayatthepointofpurchase.Whatisthatvoicesaying?

Recommendationenginesare'

ntdesignedtogiveuswhatwewant.They'

redesignedtogiveuswhattheythinkwewant,basedonwhatweandotherpeoplelikeushavewantedinthepast.

Whichmeanstheydon'

tsurpriseus.Theydon'

ttakeusoutofourcomfortzone.Arecommendationengineisn'

tthespousewhodragsyoutoanartfilmyouwouldn'

thavebeencaughtdeadatbutthenunexpectedlylove.Itwon'

tforceyoutoreadthe18thcenturycanon.It'

snosubstituteforstumblingontoagreatCDjustbecauseithascoolcoverart.RecommendationenginesaretheenemyofserendipityandGreatBooksandtheavant-garde.A19thcenturyrecommendationenginewouldneverhavesaid,IfyoulikedMonet,you'

llloveVanGogh!

Impressionismwouldhavelastedforever.

Theriskyourunwithrecommendationenginesisthatthey'

llkeepyouinarut.Theydothatbecauserutsarecomfyplaces-thoughoftenthey'

redeeperthantheylook.“Bydefinition,wekeepyouinthesam