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1、CrowdsourcingRecommendationsFromSocialSentimentYushengXie,YuCheng,DanielHonbo,KunpengZhang,AnkitAgrawal,AlokChoudharyDepartmentofElectricEngineering&ComputerScienceNorthwesternUniversity{yxi389,ych133,dkh301,kzh980,ankitag,choudhar}@eecs.northwestern.edu2145SheridanRoad,E
2、vanston,IL60201USAABSTRACTprocessingdependsheavilyonitsbuilt-inRRNwithouttakingInthispaper,weinvestigateaninnovativerecommendationadvantageofanyexternalSNSs,whichcanbeproblematicforsomee-commercebusiness.Forexample,unlikeAmazon.com,systembyincorporatingrelevantsocialopini
3、onandsentimentBestBuysuffersfromitsnot-so-activebuilt-inRRN.Itwouldbeinformation.Ourrecommendationsystem,apowerfulapplicationadvantageousifBestBuycansomehowutilizetheexistingvastofsocialsentimentanalysis,differsfrommanyexistingmodels,whichinvestigatethesituationwheretheso
4、cialnetworkitselfisnetworkofFacebook,wheremostofitsretailingbrands(e.g.structuredtoworkwiththeproductrankingandisspeciallybuiltSamsung,Dell,etc.)maintainhighprofilesocialengagementwithinsideane-commercewebsite.Incontrast,ourproposedsystemtheFacebookusers.BestBuycouldthenc
5、apitalizefromthelargefocusesonconstructingandinferringproductrecommendationsandactivesocialservicesprovidedbyFacebookwithoutfromexternalsocialnetworkservices(SNS)suchasFacebook.Inadvertisingorevendeployingitslesspopularbuilt-inRRNatall.oursystem,weprocessproductfeaturesin
6、afinite-dimensionalItischallengingtouseopinionandsentimentinformationfromapolynomiallinearspace.Additionalcomponentsofourproposedsemi-structureddatasetliketheFacebookpublicfanpagessystemincludeanasymmetricsimilaritymeasurementandan(commonlyknownasFacebookWalls)initemranki
7、ng.Dataasymmetricadvantagemeasurement.Wealsoshowthatourminingfromsemi-structuredSNSisingeneralachallengingtask.definitionsforthetwomeasurementsincludespecificpropertiesOnecannotsimplyapplysentimentalgorithmstothetextinSNSthatreducethecomputationaloverheadintheexperiments.
8、Anandexpectmiraclestohappen.Infact,findingasensiblewaytoimportantaspectofourmodelingistoincorpor
