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1、EvaluatingRecommendationSystemsGuyShaniandAselaGunawardanaAbstractRecommendersystemsarenowpopularbothcommerciallyandintheresearchcommunity,wheremanyapproacheshavebeensuggestedforprovidingrecommendations.Inmanycasesasystemdesignerthatwishestoemployarec-ommendationsystemmustchoosebetweenas
2、etofcandidateapproaches.Afirststeptowardsselectinganappropriatealgorithmistodecidewhichpropertiesoftheapplicationtofocusuponwhenmakingthischoice.Indeed,recommendationsys-temshaveavarietyofpropertiesthatmayaffectuserexperience,suchasaccuracy,robustness,scalability,andsoforth.Inthispaperwed
3、iscusshowtocomparerecom-mendersbasedonasetofpropertiesthatarerelevantfortheapplication.Wefocusoncomparativestudies,whereafewalgorithmsarecomparedusingsomeevaluationmetric,ratherthanabsolutebenchmarkingofalgorithms.Wedescribeexperimentalsettingsappropriateformakingchoicesbetweenalgorithms
4、.Wereviewthreetypesofexperiments,startingwithanofflinesetting,whererecommendationapproachesarecomparedwithoutuserinteraction,thenreviewinguserstudies,whereasmallgroupofsubjectsexperimentwiththesystemandreportontheexperience,andfi-nallydescribelargescaleonlineexperiments,whererealuserpopula
5、tionsinteractwiththesystem.Ineachofthesecaseswedescribetypesofquestionsthatcanbeanswered,andsuggestprotocolsforexperimentation.Wealsodiscusshowtodrawtrustworthyconclusionsfromtheconductedexperiments.Wethenreviewalargesetofproperties,andexplainhowtoevaluatesystemsgivenrelevantproperties.W
6、ealsosurveyalargesetofevaluationmetricsinthecontextofthepropertythattheyevaluate.GuyShaniMicrosoftResearch,OneMicrosoftWay,Redmond,WA,e-mail:guyshani@microsoft.comAselaGunawardanaMicrosoftResearch,OneMicrosoftWay,Redmond,WA,e-mail:aselag@microsoft.com1SouthChinaNormalUniversity(202.116.4
7、1.69)-2016/1/28Download2GuyShaniandAselaGunawardana1IntroductionRecommendersystemscannowbefoundinmanymodernapplicationsthatexposetheusertoahugecollectionsofitems.Suchsystemstypicallyprovidetheuserwithalistofrecommendeditemstheymightprefer,orpredicthowmuchtheymightpreferea