智能推荐算法

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1、InformationFilteringonComplexNetworks:Prediction,RankingandRecommendationLinyuanLü吕琳媛E-mail:linyuan.lue@unifr.ch2012-5-31Dep.Phys.,Univ.Fribourg1OutlineòBackground&MotivationòWhatisLinkPredicting?òHowtoRankInfluentialNodes?òWhatisPersonalizedRecommendation?òRealApplicationsòOngoingwor

2、ks2012-5-31Dep.Phys.,Univ.Fribourg2BackgroundòEveryonecancreateinformationòMoreinformationareavailableConfusionMotivationInformationOverloadòOrganization&Reconstruction&Prediction2012-5-31Dep.Phys.,Univ.Fribourg3Prediction—completepartialinformationSamplenetworks:EvolvingnetworksPredi

3、ctionofexistedyetunknownPredictionoffuturelinks,links,e.g.foodwebs,protein-proteine.g.onlinefriendshipinteractionnetworks,metabolicnetworksnetworks.Ask:whichlinkwillexist?—whowillbefriend?TargetUserAsk:whowillberecommendedAsk:whoistheleadership?touser1asfriend?Ranking—understandtheRec

4、ommendation—significantpartbasedonknownguidetheinformationinformationgeneratingSubproblemofLP2012-5-31Dep.Phys.,Univ.Fribourg4Whyitissignificant?òPrediction——Howtocompleteinformation?òReduceexperimentalcosts(missinglinks)Discoveryoflinks/interactionsofbiologicalnetworkscostsmuch.Inste

5、adofblindlycheckingallpossiblelinks,topredictinadvanceandfocusonthemostlikelyexistinglinkscansharplyreducecostsifthepredictionsareaccurateenough.òOnlinefriendshiprecommendations(futurelinks)Recommendnewfriendstotheusersinwebsociety.òApplicationsinotherfieldsItcanbeappliedinsolvingthen

6、odeclassificationprobleminpartiallylabelednetworks,improvingrecommendationforsparsesystems,reconstrcuctingnoisynetworksòRanking——Howtoidentifytheimportantparts?òInformationsecurity:publicopinionssupervision,earlywarningandcontrol.òCommercialvalue:advertisementdelivery,newsspreading.òP

7、otentiallyenhancetheactivenessandadhesivecapacityofthesocialnetwork.òRecommendation——Howtofindwhattheuserslikeefficientlyandguidetheinformationgenerating?òE-Commercialapplications2012-5-31Dep.Phys.,Univ.Fribourg5CS&PhysicsPerspectivesMethodologiesWearetryingtoexplainwhyitworkslikethis

8、!2012