推荐系统netflix获奖算法(6)

赢得netflix推荐系统大奖的算法

rule:

r ui=bui+|R(u)| 1

(ruj b

+|N(u)| j∈∑

1

uj)wijR(u)

j∈∑

cij

N(u)

Here,then2

(17)item-itemweightswijandcijrepresenttheadjustmentsweneedtomaketothepredictedratingofitemi,givenaratingofitemj.Itwasprovengreatlybene cialtousetwosetsofitem-itemweights:one(thewijs)isrelatedtothevaluesoftheratings,andtheotherdisregardstheratingvalue,consideringonlywhichitemswererated(thecijs).Theseweightsareautomaticallylearnedfromthebiases.Theconstantsb

thedatatogetherwith

ujareprecomputedaccordingto(2)–(3).

Whenadaptingrule(17)toaddresstemporaldynamics,twocomponentsshouldbeconsideredseparately.First,isthebaselinepredictorportion,bui=µ+bi+bu,whichexplainsmostoftheobservedsignal.Second,isthepartthatcapturesthemore|R(u)| 1

informativesignal,∑j∈R(u)(ruj b dealingwith)| 1user-iteminteraction

uj)wij+|N(ufromthe∑j∈N(u)cij.Forthe

baselinepart,nothingchangesfactormodel,andwemakeittime-aware,accordingtoeither(10),or(11).Thelatteroneaddsfrequenciesandisgenerallypreferred.However,capturingtemporaldynamicswithintheinteractionpartrequiresadifferentstrategy.

Item-itemweights(wijandcij)re ectinherentitemcharac-teristicsandarenotexpectedtodriftovertime.Thelearningprocessshouldmakesurethattheycaptureunbiasedlongtermvalues,withoutbeingtooaffectedfromdriftingaspects.Indeed,thetime-changingnatureofthedatacanmaskmuchofthelongertermitem-itemrelationshipsifnottreatedad-equately.Forinstance,auserratingbothitemsiandjhighinashorttimeperiod,isagoodindicatorforrelatingthem,therebypushinghigherthevalueofwij.Ontheotherhand,ifthosetworatingsaregiven veyearsapart,whiletheuser’staste(ifnotheridentity)couldconsiderablychange,thisislessevidenceofanyrelationbetweentheitems.Ontopofthis,wewouldarguethatthoseconsiderationsareprettymuchuser-dependent–someusersaremoreconsistentthanothersandallowrelatingtheirlongertermactions.

Ourgoalhereistodistillaccuratevaluesfortheitem-itemweights,despitetheinterferingtemporaleffects.Firstweneedtoparameterizethedecayingrelationsbetweentwoitemsratedbyuseru.Weadoptanexponentialdecayformedbythefunctione βu· t,whereβu>0controlstheuserspeci cdecayrateandshouldbelearnedfromthedata.Thisleadstothepredictionrule

r 1

ui=bui+|N(u)| j|cij+

(18)

j∈∑

e βu·|tui tuN(u)

|R(u)| 1

e βu·|tui tuj|((ruj b

).j∈∑

uj)wijR(u)

Theinvolvedparametersarelearnedbyminimizingtheas-sociatedregularizedsquarederror.Minimizationisperformed

bystochasticgradientdescentfor20–30iterations.Themodelisapplieddirectlytotherawdata,soallparameters(biasesandmovie-movieweights)arelearnedsimultaneously.Learning

6

ofbias-relatedparametersisgovernedbythesameconstants

discussedinSec.III.Asforthemovie-movieweights(bothwijandcij),theirlearningrateis0.005withregularizationconstantof0.002.Finally,theupdateoftheexponentβu,usesaparticularlysmallstepsizeof1e-7,withregularizationconstantequaling0.01.

Wealsoexperimentedwithotherdecayforms,likethemorecomputationally-friendly(1+βu t) 1,whichresultedinthesameaccuracy,withanimprovedrunningtime.(Noneedtochangemeta-parameters.)

Asinthefactorcase,properlyconsideringtemporaldy-namicsimprovestheaccuracyoftheneighborhoodmodel.TheRMSEdecreasesfrom0.9002[7]to0.8870(seenextsubsection).Toourbestknowledge,thisissigni cantlybetterthanpreviouslyknownresultsbyneighborhoodmethods.Toputthisinsomeperspective,thisresultisevenbetterthanthosereported[1,2,11,15]byusinghybridapproachessuchasapplyinganeighborhoodapproachonresidualsofotheralgorithms.Alessonisthataddressingtemporaldynamicsinthedatacanhaveamoresigni cantimpactonaccuracythandesigningmorecomplexlearningalgorithms.

A.What’sintheblend?

Weranthetime-awareneighborhoodmodel,withbiasesfollowing(10)for20,25,and30iterationsofstochasticgradientdescent.TheresultingRMSEswere0.8887,0.8885and0.8887,respectively.Theresultswith20and30iterationsareintheblend.

Wealsotriedextending(18)withanon-normalizedterm.Thisinvolvedaddingathirdsetofmovie-movieweights,dij,asfollows:

r 1

ui=bui+|N(u)| βu·|tui tuj|cij+

j∈∑

e N(u)

|R(u)|

1e βu·|tui tuj|((ruj b

)+j∈∑

uj)wijR(u)

j∈∑

e γu·|tui tuj|((ruj b

uj)dij).R(u)

Here,wealsotriedtoemphasizetheveryadjacentratingsmadebytheuser.Therefore,thenewdecay-controllingcon-stants,theγus,wereinitializedwitharelativelyhighvalueof0.5(comparedtoinitializingβuwithzero.)Inaddition,fordijweusedaslowerlearningrateof1e-5.Learningwasdoneby25iterationsofstochasticgradientdescent.TheresultwithRMSE=0.8881isincludedintheblend.Inretrospect,webelievethatsuchaminisculeRMSEreductiondoesnotjustifyaddingathirdsetofmovie-movieweights.

Finally,weranthetime-awareneighborhoodmodel,withbiasesfollowing(11)for20iterationsofstochasticgradientdescent.(Thethirdsetofmovie-movieweightswasnotused.)TheresultofRMSE=ter,werefertothismodelas[PQ3].