基于会话推荐的个性化系统外文翻译.docx

基于会话推荐的个性化系统外文翻译.docx

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基于会话推荐的个性化系统外文翻译

2015届

华北科技学院

本科毕业设计（论文）

文献翻译

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指导教师：

2015年6月20日

APersonalizedSystemforConversationalRecommendations

CynthiaA.Thompsoncindi@cs.utah.edu

SchoolofComputing

UniversityofUtah

50CentralCampusDrive,Rm.3190

SaltLakeCity,UT84112USA

MehmetH.G¨okermgoker@

KaidaraSoftwareInc.

330DistelCircle,Suite150

LosAltos,CA94022USA

PatLangleylangley@isle.org

InstitutefortheStudyofLearningandExpertise

2164StauntonCourt

PaloAlto,CA94306USA

Abstract

Searchingforandmakingdecisionsaboutinformationisbecomingincreasinglydifficultastheamountofinformationandnumberofchoicesincreases.Recommendationsystemshelpusersfinditemsofinterestofaparticulartype,suchasmoviesorrestaurants,butarestillsomewhatawkwardtouse.Oursolutionistotakeadvantageofthecomplementarystrengthsofpersonalizedrecommendationsystemsanddialoguesystems,creatingpersonalizedaides.Wepresentasystem{theAdaptivePlaceAdvisor{thattreatsitemselectionasaninteractive,conversationalprocess,withtheprograminquiringaboutitemattributesandtheuserresponding.Individual,long-termuserpreferencesareunobtrusivelyobtainedinthecourseofnormalrecommendationdialoguesandusedtodirectfutureconversationswiththesameuser.Wepresentanovelusermodelthatinfluencesbothitemsearchandthequestionsaskedduringaconversation.Wedemonstratetheefiectivenessofoursysteminsignificantlyreducingthetimeandnumberofinteractionsrequiredtofindasatisfactoryitem,ascomparedtoacontrolgroupofusersinteractingwithanon-adaptiveversionofthesystem.

1.IntroductionandMotivation

Recommendationsystemshelpusersfindandselectitems（e.g.,books,movies,restaurants）fromthehugenumberavailableontheweborinotherelectronicinformationsources（Burke,1999;Resnick&Varian,1997;Burke,Hammond,&Young,1996）.Givenalargesetofitemsandadescriptionoftheuser'sneeds,theypresenttotheuserasmallsetoftheitemsthatarewellsuitedtothedescription.Recentworkinrecommendationsystemsincludesintelligentaidesforfilteringandchoosingwebsites（Eliassi-Rad&Shavlik,2001）,newsstories（Ardissono,Goy,Console,&Torre,2001）,TVlistings（Cotter&Smyth,2000）,andotherinformation.

Theusersofsuchsystemsoftenhavediverse,conflictingneeds.Diffierencesinpersonalpreferences,socialandeducationalbackgrounds,andprivateorprofessionalinterestsarepervasive.Asaresult,itseemsdesirabletohavepersonalizedintelligentsystemsthatprocess,filter,anddisplayavailableinformationinamannerthatsuitseachindividualusingthem.Theneedforpersonalizationhasledtothedevelopmentofsystemsthatadaptthemselvesbychangingtheirbehaviorbasedontheinferredcharacteristicsoftheuserinteractingwiththem（Ardissono&Goy,2000;Ferrario,Waters,&Smyth,2000;Fiechter&Rogers,2000;Langley,1999;Rich,1979）.

Theabilityofcomputerstoconversewithusersinnaturallanguagewouldarguablyincreasetheirusefulnessandflexibilityevenfurther.Researchinpracticaldialoguesystems,whilestillinitsinfancy,hasmaturedtremendouslyinrecentyears（Allen,Byron,Dzikovska,Ferguson,Galescu,&Stent,2001;Dybkjfir,Hasida,&Traum,2000;Maier,Mast,&Luperfoy,1996）.Today'sdialoguesystemstypicallyfocusonhelpinguserscompleteaspecifictask,suchasplanning,informationsearch,eventmanagement,ordiagnosis.

Inthispaper,wedescribeapersonalizedconversationalrecommendationsystemdesignedtohelpuserschooseanitemfromalargesetallofthesamebasictype.Ourgoalistosupportconversationsthatbecomemoreefficientforindividualusersovertime.Oursystem,theAdaptivePlaceAdvisor,aimstohelpusersselectadestination（inthiscase,restaurants）thatmeetstheirpreferences.

TheAdaptivePlaceAdvisormakesthreenovelcontributions.Toourknowledge,thisisthefirstpersonalizedspokendialoguesystemforrecommendation,andoneoftheonlyconversationalnaturallanguageinterfacesthatincludesapersonalized,long-termusermodel.Second,itintroducesanovelmodelforacquiring,utilizing,andrepresentingusermodels.Third,itisusedtodemonstrateareductioninthenumberofsystem-userinteractionsandtheconversationtimeneededtofindasatisfactoryitem.

Thecombinationofdialoguesystemswithpersonalizedrecommendationaddressesweaknessesofbothapproaches.Mostdialoguesystemsreactsimilarlyforeachuserinteractingwiththem,anddonotstoreinformationgainedinoneconversationforuseinthefuture.Thus,interactionstendtobetediousandrepetitive.Byaddingapersonalized,long-termusermodel,thequalityoftheseinteractionscanimprovedrastically.Atthesametime,collectinguserpreferencesinrecommendationsystemsoftenrequiresformfillingorotherexplicitstatementsofpreferencesontheuser'spart,whichcanbedificultandtimeconsuming.Collectingpreferencesinthecourseofthedialogueletstheuserbeginthetaskofitemsearchimmediately.

Theinteractionbetweenconversationandpersonalizedrecommendationhasalsoaffectedourchoicesfortheacquisition,utilization,andrepresentationofusermodels.TheAdaptivePlaceAdvisorlearnsinformationaboutusersunobtrusively,inthecourseofanormalconversationwhosepurposeistofindasatisfactoryitem.Thesystemstoresthisinformationforuseinfutureconversationswiththesameindividual.Bothacquisitionandutilizationoccurnotonlywhenitemsarepresentedtoandchosenbytheuser,butalsoduringthesearchforthoseitems.Finally,thesystem'srepresentationofmodelsgoesbeyonditempreferencestoincludepreferencesaboutbothitemcharacteristicsandparticularvaluesofthosecharacteristics.Webelievethattheseideasextendtoothertypesofpreferencesandothertypesofconversations.

Inthispaper,wedescribeourworkwiththeAdaptivePlaceAdvisor.Webeginbyintroducingpersonalizedandconversationalrecommendationsystems,presentingourdesigndecisionsalongtheway.InSection3wedescribethesystemindetail,whileinSection4wepresentourexperimentalevaluation.InSections5and6wediscussrelatedandfuturework,respectively,andinSection7weconcludeandsummarizethepaper.

2.PersonalizedConversationalRecommendationSystems

Ourresearchgoalsaretwo-fold.First,wewanttoimprovebothinteractionqualityinrecommendationsystemsandtheutilityofresultsreturnedbymakingthemuseradaptiveandconversational.Second,wewanttoimprovedialoguesystemperformancebymeansofpersonalization.Assuch,ourgoalsforusermodelingdifierfromthosecommonlyassumedinrecommendationsystems,suchasimprovingaccuracyorrelatedmeasureslikeprecisionandrecall.Ourgoalsalsodifierfromthatofpreviousworkinusermodelingindialoguesystems（Haller&McRoy,1998;Kobsa&Wahlster,1989;Carberry,1990;Kass,1991）,whichemphasizestheabilitytotracktheuser'sgoalsasadialogueprogresses,butwhichdoesnottypicallymaintainmodelsacrossmultipleconversations.

Ourhypothesisisthatimprovementsinefficiencyandeffectivenesscanbeachievedbyusinganunobtrusivelyobtainedusermodeltohelpdirectthesystem'sconversationalsearchforitemstorecommend.Ourapproachassumesthatthereisalargedatabaseofitemsfromwhichtochoose,andthatareasonablylargenumberofattributesisneededtodescribetheseitems.Simplertechniquesmightsufficeforsituationswherethedatabaseissmalloritemsareeasytodescribe.

2.1Personalization

Personalizeduseradaptivesystemsobtainpreferencesfromtheirinteractionswithusers,keepsummariesofthesepreferencesinausermodel,andutilizethismodeltogeneratecustomizedinformationorbehavior.Thegoalofthiscustomizationistoincreasethequalityandappropriatenessofboththeinteractionandtheresult（s）generatedforeachuser.

Theusermodelsstoredbypersonalizedsystemscanrepresentstereotypicalusers（Chin,1989;Rich,1979）orindividuals,theycanbehand-craftedorlearned（e.g.,fromquestionnaires,ratings,orusagetraces）,andtheycancontaininformationaboutbehaviorsuchaspreviouslyselecteditems,preferencesregardingitemcharacteristics（suchaslocationorprice）,orpropertiesoftheusersthemselves（suchasageoroccupation）（Kobsa&Wahlster,1989;Rich,1979）.Also,somesystemsstoreusermodelsonlyforthedurationofoneinteractionwithauser（Carberry,Chu-Carroll,&Elzer,1999;Smith&Hipp,1994）,whereasothersstorethemoverthelongterm（Rogers,Fiechter,&Langley,1999;Billsus&Pazzani,1998）.

Ourapproachistolearnprobabilistic,long-term,individualusermodelsthatcontaininformationaboutpreferencesforitemsanditemcharacteristics.Wechoselearnedmodelsduetothedificultyofdevisingstereotypesorreasonableinitialmodelsforeachnewdomainencountered.Wechoseprobabilisticmodelsbecauseoftheirflexibility:

asingleusercanexhibitvariablebehaviorandtheirpreferencesarerelativeratherthanabsolute.Long-termmodelsareimportanttoallowinfluenceacrossmultipleconversations.Also,asalreadynoticed,difierentusershavedifierentpreferences,sowechoseindividualmodels.Finally,preferencesaboutitemsanditemcharacteristicsareneededtoinfluenceconversationsandretrieval.

Oncethedecisionismadetolearnmodels,anotherdesigndecisionrelatestothemethodbywhichasystemcollectspreferencesforsubsequentinputtothelearningalgorithm（s）.Herewecandistinguishbetweentwoapproaches.Thedirectfeedbackapproachplacesthe