模拟国际会议PPT

模拟国际会议PPT

一、基本内容

标题页、目录页、章节内容、声明、参考文献、致谢

其中，章节内容通常包括主题介绍、实验或者计算过程、结果、结论或总结二、PPT制作步骤

1)确定章节内容，对各部分内容进行逻辑性分析和重要性排序2)PPT初步成型3)PPT详细设计4)检查完善三、设计原则

目的明确、思路清晰、逻辑性强

文字、表格、图表合理搭配，并善于使用结构图简洁大方、有较好的视觉效果四、设计内容版式设计模板设计配色设计动画设计切换设计效果设计说明：

1)PPT是辅助说明的工具，使表达内容达到易于接受、赏心悦目的效果。2)PPT制作熟能生巧，注意搜集好的设计和素材，制作时信手拈来。

3)PPT的使用效果与演讲者的表达技巧密切相关，演讲者应该以饱满的热情，尽力将自己

熟知的内容分享给观众。

扩展阅读：模拟国际会议演讲稿

Recsplorer:RecommendationAlgorithmsBasedonPrecedenceMining

1.Introduction

Thankyouverymuch,Dr.Li,foryourkindintroduction.Ladiesandgentlemen,Goodmorning!Iamhonoredtohavebeeninvitedtospeakatthisconference.BeforeIstartmyspeech,letmeaskaquestion.Doyouthinkrecomemdationsfromothersareusefulforyourinternetshopping?Thankyou.Itisobviousthatrecommendationsplayanimportantroleinourdailyconsumptiondecisions.

Today,mytopicisaboutRecommendationAlgorithmsBasedonPrecedenceMining.Iwanttoshareourinterestingresearchresultonrecommendationalgorithmswithyou.Thecontentofthispresentationisdividedinto5parts:insession1,Iwillintruducethetradictionalrecommendationandournewstrategy;insession2,IwillgivetheformaldefinitionofPrecedenceMining;insession3,Iwilltalkaboutthenovelrecommendationalgorithms;experimentalresultwillbeshowedinsession4;andfinally,Iwillmakeaconclusion.

2.Body

Session1:Introduction

Thepictureonthisslideisaninstanceofrecommemdationapplicationonamazon.

Recommendersystemsprovideadviceonproducts,movies,webpages,andmanyothertopics,andhavebecomepopularinmanysites,suchasAmazon.Manysystemsusecollaborativefilteringmethods.ThemainprocessofCFisorganizedasfollow:first,identifyuserssimilartotargetuser;second,recommenditemsbasedonthesimilarusers.Unfortunately,theorderofconsumeditemsisneglect.Inourpaper,weconsideranewrecommendationstrategybasedonprecedencepatterns.Thesepatternsmayencompassuserpreferences,encodesomelogicalorderofoptionsandcapturehowinterestsevolve.

Precedenceminingmodelestimatetheprobabilityofuserfutureconsumptionbasedonpastbehavior.Andtheseprobabilitiesareusedtomakerecommendations.Throughourexperiment,precedenceminingcansignificantlyimproverecommendationperformance.Futhermore,itdoesnotsufferfromthesparsityofratingsproblemandexploitpatternsacrossallusers,notjustsimilarusers.

Thisslidedemonstratesthedifferencesbetweencollaborativefilteringandprecedencemining.Supposethatthescenarioisaboutcourseselection.Eachquarter/semesterastudentchoosesacourse,andratesitfrom1to5.Figurea)showsfivetranscripts,atranscriptmeansalistofcourse.Uisourtargetstudentwhoneedrecommendations.Figureb)illustrateshowCFwork.Assumesimilarusersshareatleasttwocommoncoursesandhavesimilarrating,thenu3andu4aresimilartou,andtheircommoncoursehwillbearecommendationtou.Figurec)presentshowprecedenceminingwork.Forthisexample,weconsiderpatternswhereonecoursefollowsanother.Supposepatternsoccouratleasttwotranscripsarerecognizedassignificant,then(a,d),(e,f)and(g,h)arefoundout.Andd,h,andfarerecommendationtouwhohastakena,gande.

NowIwillaprobabilisticframeworktosolvetheprecedenceminingproblems.Ourtargetuserhasselectedcoursea,wewanttocomputetheprobabilitycoursexwillfollow,i.e.,Pr[x|a].

howerve,whatwereallyneedtocalculateisPr[x|aX]ratherthanPr[x|a].Becauseinourcontext,wearedecidingifxisagoodrecommendationforthetargetuserthathastakena.Thusweknowthatourtargetuser’stranscriptdoesnothavexbeforea.Forinstance,thetranscriptno.5willbeomitted.Inmorecommonsituation,ourtargetuserhastakenalistofcourses,T={a,b,c,…}not

justa.Thus,whatreallyneedisPr[x|TX].Thequestionishowtofigureoutthisprobability.Iwillansweritlater.

Session2:PrecedenceMining

WeconsiderasetDofdistinctcourses.Weuselowercaseletters(e.g.,a,b,…)torefertocoursesinD.AtranscriptTisasequenceofcourses,e.g.,a->b->c->d.ThenthedefinitionofTop-kRecommendationProblemisasfollows.GivenasettranscriptsoverDfornusers,theextratranscriptTofatargetuser,andadesirednumberofrecommendationsk,ourgoalisto:

1.Assignascorescore(x)(between0and1)toeverycoursex∈Dthatreflectshowlikelyitisthetargetstudentwillbeinterestedintakingx.Ifx∈T,thenscore(x)=0.

2.Usingthescorefunction,selectthetopkcoursestorecommendtothetargetuser.Tocomputescores,weproposetousethefollowingstatistics,wherex,y∈D:f(x):thenumberoftranscriptsthatcontainx.

g(x;y):thenumberoftranscriptsinwhichxprecedescoursey.

Thisslideshowsthecalculationresultoff(x)andg(x,y).Forexample,fromthetable,weknowthatf(a)is10andg(a,c)is3.

WeproposeaprecedenceminingmodeltosolvetheTop-kRecommendationProblem.Hereare

somenotation:xy,whichwehavememtionedinsession1,referstotranscriptwherexoccurswithoutaprecedingy;xyreferstotranscriptwherexoccurswithoutyfollowingit.Weusequantitiesf(x)andg(x,y)tocompteprobabilitiesthatencodetheprecedenceinformation.Forinstance,fromformular1to7.Iwouldnottellthedetailofallformulars.Wejustpayattentionto

formular5,notethatthisquantityaboveisthesameas:Pr[xy|yx]whichwillbeusedtocomputescore(x).

Asweknow,thetargetuserusuallyhastakenalistofcoursesratherthanacourse,soweneedto

extentourprobabilitycalculationformulars.Forexample,supposeT={a,b},Pr[xT]theprobabilityxoccurswithouteitheranaorbprecedingit;Pr[xT]theprobabilityxoccurswithouteitheranaorbfollowingit.Thisprobabilitycanbecalculatedexactly.Sohowtocalculateit?

Session3:RecommendationAlgorithms

Let’sreviewsession2.Themaingoaloftherecommendationalgorithmsistocalculatethescore(x),andthenselectthetopkcoursesbasedonthesescores.TraditionalrecommendationalgorithmscomputearecommendationscoreforacoursexinDonlybasedonitsfrequencyofoccurence.Itdoesnottakeintoaccountthecoursestakenbythetargetuser.

OurrecommendationalgorithmscalledSingleMCconquertheshortcomingofthetraditionalones.Itcomputesthescore(x)usingtheformular5.Thedetailisasfollows:astudentwithatranscripToftakencourses,forthecoursey∈T,ifyandxappeartogetherintranscriptssatisfiesthe

thresholdθ,thencomputethePr[xy|yx],reflectingthelikelihoodthestudentwilltakecoursex

andignoringtheeffectoftheothercoursesinT;finallythemaximumofPr[xy|yx]ischoosenasthescore(x).

Hereisthecalculationformularofscore(x)ofSignleMC.Forexample,withthehigerscore,dwillberecommended.

AnothernewrecommendationalgorithmnamedJointProbabilitiesalgorithm,JointPforshort,isproposed.UnlikeSingleMC,JointPtakesintoaccountthecompletesetofcoursesinatranscript.Informular12,wecannotcomputeitsquantityexactly,Rememberthisproblemwehavementioned.Oursolutionistouseapproximations.Thisslideisaboutthefirstapproximatingformular.Andthisthesecondapproximatingformular.

ThesystemiscourseRand,anddatasetforexperimentcontains7,500transcripts.

Thisslideshowsthenewrecommendationalgoritmswithblackcolorandthetraditionaloneswithbluecolor.

Thechartonthisslideindicatesournewrecommendationalgorithmsbeatthetraditionalonesinprecision,becausetheformeronesexploitpatternsacrossallusers,whilethelatteronesjustusethesimilarusers.

Thechartonthisslidepointsoutournewrecommendationalgorithmsalsobeatthetraditionalonesincoverageforthesamereason.

Session5:ConclusionandSummary

Inconclusion,weproposedanovelprecedenceminingmodel,developedaprobabilisticframeworkformakingrecommendationsandimplementedasuiteofrecommendationalgorithmsthatusetheprecedenceinformation.Experimentalresultshowsthatournewalgorithmsperformbetterthanthetraditionalones,andourrecommendationsystemcanbeeasilygeneralizedtootherscenarios,suchaspurchasesofbooks,DVDsandelectronicequitment.

Tosumup,first,Iintroducedthemotivationandtheoutlineofwork;second,Igavethedefinitionofprecedenceminingmodel;third,Idescribedsomenewrecommendationalgorithmsusingprecedenceinformation;forth,Ishowedourexperimentalresultstocomparethenewalgorithmswithtraditionalones.Finally,Imadeaconclusionofourwork..

That’sall.Thankyou!Arethereanyquestions?

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