基于网络表示学习的新闻用户影响力预测-豆柴文库

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基于网络表示学习的新闻用户影响力预测
Title:NewsUserInfluencePredictionBasedonNetworkRepresentationLearning
Abstract:
Thepredictionofnewsuserinfluenceplaysacrucialroleinvariousapplications,includingpersonalizedrecommendationsystems,viralmarketing,andsocialnetworkanalysis.Traditionalmethodsoftenrelyonshallowfeaturesandoverlookthecomplexrelationshipsamongnewsusers.Inrecentyears,networkrepresentationlearninghasemergedasapowerfulapproachforcapturingtherichstructuralinformationofnetworks.Inthispaper,weproposeanovelmethodthatcombinesnetworkrepresentationlearningwithnewsuserinfluenceprediction.Ourmethodleveragestheinherentrelationshipsamongnewsusersinanetworktoimprovetheaccuracyofinfluenceprediction.Weconductexperimentsonareal-worldnewsdatasetandcompareourmethodwithstate-of-the-artinfluencepredictionalgorithms.Theresultsdemonstratetheeffectivenessofourapproachinaccuratelypredictingnewsuserinfluence.
1.Introduction
Withtheriseofonlinesocialnetworksandnewsplatforms,understandingandpredictinguserinfluencehasbecomeacriticaltask.Newsuserinfluencepredictionaimstoestimatetheextenttowhichausercaninfluenceothersinspreadinginformationorshapingopinions.Accuratepredictionofuserinfluencecanprovidevaluableinsightsforpersonalizedrecommendationsystems,socialnetworkanalysis,andtargetedmarketingcampaigns.Traditionalinfluencepredictionmethodsoftenrelyonshallowfeatures,suchasuserdemographicsoractivitypatterns,whichmaynotcapturethecomplexinteractionsandrelationshipsamongnewsusers.Inrecentyears,networkrepresentationlearninghasshownpromiseincapturingtherichstructuralinformationofnetworks,makingitanidealapproachfornewsuserinfluenceprediction.
2.RelatedWork
Thissectionprovidesanoverviewofexistingapproachesfornewsuserinfluenceprediction.Traditionalmethodsoftenrelyonfeatureengineering,whichmanuallyselectsandconstructsstaticfeaturestorepresentusers'characteristicsandinteractions.Althoughthesemethodshaveachievedcertainlevelsofsuccess,theyoftenfacechallengesincapturingthecomplexanddynamicrelationshipsamongnewsusers.Recen