预览加载中，请您耐心等待几秒...

基于卷积-LSTM网络的广告点击率预测模型研究.docx

预览
1/2
2/2

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

基于卷积-LSTM网络的广告点击率预测模型研究 Title:ResearchonConvolutional-LSTMBasedModelforClick-ThroughRatePredictioninAdvertising Abstract: Withtherapidgrowthofonlineadvertising,accuratelypredictingtheclick-throughrate(CTR)hasbecomecrucialforadvertiserstooptimizetheirmarketingcampaigns.Inrecentyears,deeplearningmodelshaveshownpromisingresultsinCTRpredictiontasks.Thispaperproposesanovelmodelthatcombinesthepowerofconvolutionalneuralnetworks(CNNs)andlongshort-termmemory(LSTM)networkstoaccuratelyforecastCTRinthefieldofadvertising. 1.Introduction: ThesignificanceofaccuratelypredictingCTRliesinenablingadvertiserstomaximizetheeffectivenessoftheirmarketingcampaignsbytargetingtherightaudienceandoptimizingadvertisingresources.TraditionalCTRpredictionmodels,suchaslogisticregressionanddecisiontree-basedmethods,arelimitedintheirabilitytocapturecomplexpatternsandinherentdependenciesamonguserbehaviorsandadvertisementfeatures. 2.RelatedWork: ThissectionreviewsrelevantliteratureonCTRpredictionmodels.Itcoversbothtraditionalstatisticalmethodsandrecentdeeplearningtechniques,highlightingtheadvantagesandlimitationsofeachapproach.ItalsodiscussestheapplicationofCNNsandLSTMsinvariousdomainsandtheirpotentialforimprovingCTRpredictionaccuracy. 3.Methodology: TheproposedmodelleveragesbothCNNsandLSTMstoextracthigh-levelfeaturesfromrawdatarepresentations.TheCNNcomponentcaptureslocalspatialdependenciesbyapplyingconvolutionalfilterstoadvertisementimagesortextualdescriptions.TheextractedfeaturesarethenfedintoanLSTMlayertocapturetemporaldependenciesinuserbehaviorsovertime.Thisarchitectureallowsthemodeltoeffectivelylearncomplexinteractionsbetweentheadvertisementanduserdata. 4.ExperimentalSetup: Theexperimentalsetupdescribesthedatasetusedformodeltrainingandevaluation,aswellaspreprocessingtechniquesemployedtohandlemissingdata,handlecategoricalfeatures,andnormalizeinputvalues.Italsopresentsevaluationmetricsutilizedtoassesstheperformanceoftheproposedmodelandcomparesitwithbaselinesandstate-of-the-artmethods. 5.ResultsandAna