模擬國際會議PPT
一、基本內(nèi)容
標(biāo)題頁、目錄頁、章節(jié)內(nèi)容、聲明、參考文獻(xiàn)、致謝
其中,章節(jié)內(nèi)容通常包括主題介紹、實(shí)驗(yàn)或者計(jì)算過程、結(jié)果、結(jié)論或總結(jié)二、PPT制作步驟
1)確定章節(jié)內(nèi)容,對各部分內(nèi)容進(jìn)行邏輯性分析和重要性排序2)PPT初步成型3)PPT詳細(xì)設(shè)計(jì)4)檢查完善三、設(shè)計(jì)原則
目的明確、思路清晰、邏輯性強(qiáng)
文字、表格、圖表合理搭配,并善于使用結(jié)構(gòu)圖簡潔大方、有較好的視覺效果四、設(shè)計(jì)內(nèi)容版式設(shè)計(jì)模板設(shè)計(jì)配色設(shè)計(jì)動畫設(shè)計(jì)切換設(shè)計(jì)效果設(shè)計(jì)說明:
1)PPT是輔助說明的工具,使表達(dá)內(nèi)容達(dá)到易于接受、賞心悅目的效果。2)PPT制作熟能生巧,注意搜集好的設(shè)計(jì)和素材,制作時信手拈來。
3)PPT的使用效果與演講者的表達(dá)技巧密切相關(guān),演講者應(yīng)該以飽滿的熱情,盡力將自己
熟知的內(nèi)容分享給觀眾。
擴(kuò)展閱讀:模擬國際會議演講稿
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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