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1、AttributeAwareAnonymousRecommenderSystemsManuelStritt,KarenH.L.Tso,LarsSchmidt-ThiemeComputer-basedNewMediaGroup,DepartmentofComputerScienceAlbert-Ludwigs-Universit¨atFreiburg,Germany{stritt,tso,lst}@informatik.uni-freiburg.deSummary.Anonymousrecommendersystemsaretheelectronicpe
2、ndanttoven-dors,whoaskthecustomersafewquestionsandsubsequentlyrecommendproductsbasedontheanswers.Inthisarticlewewillproposeattributeawareclassifier-basedapproachesforsuchasystemandcompareittoclassifier-basedapproachesthatonlymakeuseoftheproductIDsandtoanexistingknowledge-basedsyst
3、em.Wewillshowthattheattribute-basedmodelisveryrobustagainstnoiseandprovidesgoodresultsinalearningovertimeexperiment.1IntroductionRecommendersystems(RS)areusedbyonlinecommercialsites,e.g.ama-zon.comandebay.com(giftfinder),tohelpuserstofindproductsthatfittheirpreferences.Theyuseuserp
4、rofilesthatcontainpreferenceindicatorsforspecificproductsortypesofproductstoassesstheinterestacustomermayhaveinproductofferingsandrecommendthoseheisassumedtolikemost.Preferencescaneitherbespecifieddirectlybycustomers,i.e.,byratingprod-ucts,orindirectlyindicatedbytheirbehavior,e.g.,s
5、earchkeywords,productsviewedindetail,productsputinthemarketbasketorproductspurchasedaswellasfrequenciesanddurationsofsuchevents.Ingeneral,therearetwotypesofRS[10]:(i)RSwithuseridentificationthatrequireausertologin(orrelyonotherunsafeuseridentificationmechanismssuchascookies)andthe
6、reforecanaccumulatepreferenceindicators,e.g.,purchasehistoriesovertime.Wheneveracustomerlogsinthesystem,hispastprofileislookedupandusedforrecommending.Newusershavetoprovidesomefront-upinformation,e.g.,someinitialproductratings,beforetheycanusethesystem.Thisissometimescalledthenew
7、userproblem[9].(ii)Anonymousrecommendersystemsthatdonotrequireauseridentifi-cationandthereforedonothaveanyinitialinformationaboutacustomer.Asinthecontextofe-commercedirectratingsandusage-basedpreferenceindica-torsaretootimeconsumingtocollectforsingleuse,othermechanismshaveto2Manu
8、elStritt,KarenH.L.Tso,LarsSchmidt-Thiemebeusedt
