A Smart Targeting System for Online Advertising

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1 778 JOURNAL OF COMPUTERS VOL 4 NO 8 AUGUST 009 A Smar Targeng Sysem for Onlne Adversng Weh Da School of Managemen Fdan Unversy Shangha PRChna Emal: hda@fdanedcn Xngyn Da and Tao Sn School of Managemen Fdan Unversy Shangha PRChna Emal: {xngynda sephensn004}@63com Absrac Wh he rapd ncrease of onlne adversemens n mareng as ell as he ser's aenon resorces are becomng scarce argeng echnqe s appled o mprove he effcency and effecveness of onlne adversng by delverng he rgh adversemen o he rgh adence a he rgh me and saon and h he rgh mehod The aenon of crren argeng s ransformed from conen argeng freqency argeng me argeng and geographcal argeng o he exended argeng based on ser s characerscs h daa mnng echnqe and behavor modelng echnqe on he analyss of sers The proecon of prvacy has been an arged sse along h he applcaon of above echnqe Ths paper presens a smar argeng sysem h hgh effcency effecveness and proecon of prvacy I ses Web conen mnng and Web sage mnng echnqes o rac and mne he ser behavors hdng n he hsorcal and crren ser sessons and desgns nerfaces for mananng he adversng rles hch can conrol he argeng sysem o aomacally delver he personalzed adversemens All he relaed noledge and rles are negraed by one nfed vecor space model The process of argeng doesn reqre any sensve daa np by sers so her prvacy s proeced Index Terms onlne adversng eb adversng personalzaon behavoral argeng eb mnng I INTRODUCTION The emergence and developmen of onlne adversemens are sbeced o hree drec nflences sch as he qany of ebses he qany of onlne csomer servces and he promoon of broadband A presen hese hree condons have become mare h he rapd applcaon of Inerne Accordng o Research s predcon for example Chna's onlne adversng mare ll have 30% compond groh n he nex fe years and reach 6 bllon n 009[] Wh he grong proporon of onlne adversemens n mareng as ell as he ser's aenon resorces are becomng scarce people are eager o rely on he advanced echnology o solve he problems ha can be Manscrp receved Feb 0 009; revsed Aprl Ths research as sppored by Naonal Hgh-ech R & D Program (863 Program) of Chna (No008AA04Z7) Naonal Naral Scence Fondaon of Chna (No704000)and Shangha Leadng Academc Dscplne Proec (NoB0) solved by radonal adversng sch as I no I have ased half of he money n adversng b I don no hch half Therefore f e send adversemens o he rgh adence hrogh onlne adversemens argeng echnology can mprove he effcency of he onlne adversemens plaform mae vsors become byers save he cos of adversers and mprove csomer s loyaly I s grealy valable for oday s companes ho s nder he shor-lfe and hghly complcaed and compeve bsness envronmens [] [3] To reach he goal he sysem needs horoghly ndersand ser s crren neress esablsh proper sysem archecre and proec he ser s prvacy a he same me Some scholars carry o a large nmber of qanave and modelng researches on onlne adversng argeng sysem Aggaral CC (998)poned o ha mos of he onlne adversng sysem sed ser-based argeng mehod for he banner adversemens and nrodced a nmber of sascs opmzaon and schedlng model [4] Mobasher B (00) proposed he se of assocaed mnng rles based on ser behavors o provde an effecve and exendable csomzed ebpage echnology [5] Mlan A ( ) presened he se of fzzy smlary algorhm [6] [7] and gave a general adapve onlne servce archecre based on evolonary genec algorhm [8] Scholars n he research of personalzed neor servces arged ha s necessary o have argeng sysem from conen argeng freqency argeng me argeng and geographcal argeng o he exended argeng based on ser s characerscs [9]-[] Recenly scholars have expressed her concern abo ser s prvacy sses n personalzed recommendaon sysem [3]-[6] Kazeno Pand Adams M(004) nrodced AD ROSA sysem for negrang ser behavor and conen mnng echnologes o redce he ser's np and proec he prvacy[6] Therefore or paper s amed o address he frher research on ST (Smar Targeng) sysem o mee he crren developmen rend of onlne adversemens dsplayng he adversemens o he rgh adence and proecng her prvaces effcenly and effecvely I ses Web conen mnng and Web sage mnng echnqes o rac and mne he ser behavors hdng n he hsorcal and crren ser sessons and desgns 009 ACADEMY PUBLISHER

2 JOURNAL OF COMPUTERS VOL 4 NO 8 AUGUST nerfaces for mananng he adversng rles hch can conrol he argeng sysem o aomacally delver he personalzed adversemens All he relaed noledge and rles are negraed by one nfed vecor space model The process of argeng doesn reqre any sensve daa np by sers so her prvacy s proeced II TARGETING SYSTEM FOR ONLINE ADVERTISING A Concep and Operaon In order o realze argeng for adversng he smar argeng (ST) sysem e presened here ms have some basc fncons: ebpage and he ser relaed daa collecon eb conen modelng and model pdang ser modelng and model pdang and argeng [5] We can see he concep of ST sysem n Fg Fg Concep of ST Sysem Afer ser s frs reqes for a ebpage sar a ne sesson and hen he sysem ll collec every sep of he ser s assess behavor ncldng URL me and behavor of clcs We can no he conen ha he/she neresed n and he access mode he/she prefer These facors can provde he parameers hen e an se he rgh adversemens o he ser Fg shos he operaon of ST sysem We have follong seps o reach he goal: ) Daa Collecon Throgh he se of Web daa mnng echnology o exrac ebpage conen and he hsory of he ser sesson daa as ell as rac and dg he crren ser s behavors e can ge he ser s neres and preferences ) Paern Recognon We can ge he ypcal behavor of a grop of sers by clserng he eyords and ser sesson afer eyord exracon 3) User Modelng For each grop of sers ho have smlar access behavors e esablsh her shor-rn and long-rn ser model 4) User Machng For each ser e mach hm/her o he proper ser model based on he ser's crren neres or long-rn neres 5) Targeng We se dfferen adversemen opons for he sers by dfferen ser model h approprae adversemen rles o acheve he prposes of argeng Fg Operaon of ST Sysem 009 ACADEMY PUBLISHER

3 780 JOURNAL OF COMPUTERS VOL 4 NO 8 AUGUST 009 B Daa Collecon ST sysem ses he eb log forma ha s compable h W3C [7] The forma s as follos: ST sysem ses sd (SessonID) o record and recognze he ser s sesson [8] d o record and recognze ser s nqe ID Referer s he comng pah of crren page We can no he me he ser spended a dfferen pages h referrer and r The oher parameers please refer o he docmen [7] The dsplay and clc of he adversemens have he same log forma: Type s sed o dsngsh dsplay from clc Tme c- p and referrer can be dged by he adversemen engne hle oher parameers are ransferred by lns Referer represens he paren ln advd represens he ID of adversemen and camd represens he ID of adversemen acvy C Paern Recognon A hs sage e shold ge all he eyords and hen clser hem Here e se ord segmenaon mehod o ge he eyords Frsly e can ge he segmenaon resl from boh sdes based on dconary processng Secondly some rles are sed o dsambgae based on sascs processng Fnally save he prelmnary eyords d = { L } from he eb space Q = { p p L pl} o he daabase Nex he prelmnary eyords shold be clsered We sppose he page vecor of eyords s p p p L L p p p p = L L M M M M p p p L Q Q QL L s he qany of ebpage n eb space From he nformaon rereval heory[89] e have he coordnae: p L = ( f b d + α f + βf + γf ) log( ) [ Q] [ L] n b represens he egh of eyord n page p and f f d f and f represens he freqency ha eyord appear n body le descrpon and he hole eyords respecvely α β γ are he eyord s mporance coeffcens n le descrpon and he hole eyords n s he qany of ebpage ha nclde he eyord A mehod named HACM can be sed o fnd he Jaccard coeffcen hch can help s o ge he grop Jaccard coeffcen L p p = sm ( p p) = L p + L p L p p ( ) ( ) = = = [ L] The hole eyords become K grops and he mean eyord eb vecor = cp n = p can n represens he feare of grop n s he nmber of eyords of grop Smlarly e can ge mean eyord adversemen vecor ca = n = a { a a a } n a= L M s he adversemen vecor of he eyord belong o grop Smlarly e can have he sers sessons clsered = { L N } represens all he sers and { sa sa sa sa s a = L M } ( represens he nmber ha adversemen s beng reqesed drng he sesson of se can be normalzed o[0])s all ser s sa sesson vecor Afer clserng h HACM e can ge he Jaccard coeffcen grop mean ser s sesson vecor and mean adversemen access vecor as follos: L sp sp = sms ( p s p) = L sp + L sp L sp sp ( ) ( ) = = = csp = n = s p n csa = n = s a n ' D User Modelng In ST sysem here are o nd of ser model One s shor-erm ser model and he oher s long-erm ser model ) Shor-rn ser model In he shor-erm ser model he coordnae of asp asp asp ebpage access vecor asp = { L L } s as follos: f p () asp asp' = δ f p () 0 f p (0) δ [0] s consan () s he ebpage collecon hch has s been accessed () s he ebpage collecon hch as accessed before and (0) s he 009 ACADEMY PUBLISHER

4 JOURNAL OF COMPUTERS VOL 4 NO 8 AUGUST ebpage collecon hch has no been accessed ye We asp ' asp can assme δ = 0 7 s dfferen from he before pdae The vecor of he me for he sesson lasng asp asp asp asp = { L } L can be con form smmng he me he ser spends on all pages and hen asp normalzed o [ 0] [ L] The coordnae of adversemen dsplay vecor { = L M } s as follos: f a a() = ' μ f a a() 0 f a a(0) μ [0] s consan Here e assme μ = 0 8 a () s he adversemen collecon hch has s been appeared a () s he adversemen collecon hch as appeared before and a (0) s he adversemen collecon hch has no been appeared ye ' s dfferen from he before pdae The coordnae of ser s search behavor drng crren q q q Q sesson vecor q = { L } s as follos: f () q q' = ρ f () 0 f (0) ρ [0] s consan Here e assme ρ = 0 85 () s he eyord collecon hch has s been searched () s he eyord collecon hch as searched before and (0) s he eyord collecon hch has no been searched ye s dfferen form he q before q ' pdae ) Long-rn ser model In he long-rn ser model all he hsory sesson vecor of User can be record becase of he nqe ID n ST sysem ncldng he sesson vecor sp ha represens he saon he ser access ebpage sa ha represens he saon he ser access adversemens q ha represens he eyords for he ser s searchng and sp ha represens he me for he sessons lasng Then e have sp sp sp sp { =< s p s p L s np >= } L L sp represens he sesson of ser and sp y n page y represens he long-rn egh for neres of ser Smlarly e have sa q and sp sp sa and q can be calclaed as follos: = n sp l s p = = n sa h s a = = n q D q = l [0] h [0] D [0] are he dscon coeffcens We assme l = 0 6 h = 0 65 and D = 07 s smaller hen s more near presen and he of crren ser s sesson s The same o shor-rn ser model e ll consder he me ha he ser spends on ebpage Smlarly normalze he vecor sp = n s p = nl he sp coordnaon [ 0] [ L] E User Machng Also e have o ays o mach he ser o he proper ser model One s based on he ser s crren neres and he oher s based on he ser s long-rn neres ) Machng based on crren neres To mach he ser o he proper ser model e shold fnd he smalles cos( as cp ) and cos( asp csp ) as s he vecor ha represens he ser s neres ( as = asp asp he symbol represens ha he correspondng coordnaes of crren sesson vecor asp mlpled by ebpage me vecor asp ) The smaller cosne vale s he hgher smlary We can calclae he cosne vale as follos: cos( as cp ) = L as cp = L as = L cp = ( ( ) ) cos( asp csp ' ) = L asp csp = ' L asp = L csp = ( ) ( ) ' As o he vecor q e have anoher vecor aqa aqa aqa aqa = { L M } ha represens he nflence of ser s searchng behavor o he egh of adversemen Then e ge Q q = a aqa = Q q = ) Machng based on long-rn neres 009 ACADEMY PUBLISHER

5 78 JOURNAL OF COMPUTERS VOL 4 NO 8 AUGUST 009 There are o mehods for machng based on long-rn neres The frs one s no only concernng he crren access behavor of he ser b also concernng he hsory of sesson Then e can se sp q and nsead of asp q and based on crren neres e can ge sp asp Smlar o machng as ca csa ' The second one s concernng he ser s and aqa neres refleced from he hsory Then assme =0 hen calclae sp sa Therefore as ca and asp and aqa are all e need q F Targeng Afer nong he ser s ser model s me o mae he rgh adversemen for hm/her h some adversemen rles Noadays some ne rles are appeared along h he developmen of onlne adversng sch as CPI(Cos Per Impresson) CPM (Cos Per Monh) CPC(Cos Per Clc) CPS (Cos Per Sale) CPA(Cos Per Acon) No e dscss he mehod for argeng ) Targeng h he crren neres Afer geng all he relave vecors e can creae a sor vecor ran for personalzed adversemen The vecor can be calclaed as follos: ran = s a) ( ) ep ap ( ξ ca + ψaqa + ζcsa ) ( ' ep ep ep ep = { L } M represens heher he adversemen s alloed o dsplay drng he sesson of ep f > eq he ser = 0 ohers The coeffcens ξ ψ and ζ mporance of he vecor Here e assme = ca aqa and ξ ψ = and ζ = 0 9 represens he csa ' n ran ran ncldes nearly all he necessary nformaon For example ( s a) ll mae he adversemens ha he ser has clced ge he smaller egh drng he argeng laer ( ) ll mae he adversemen dsplayed neares ge he smaller egh ep and ap are pars of he adversemen rles and ca aqa and csa ' reflec he conen and access behavor mode ha he ser neresed n Every HTTP reqes ll pdae ran and hen reflec characerscs of he ser n me ) Targeng h he long-rn neres For he condon ha concern abo he crren saon and hsory ran ll also reflec he sesson hsory and he recenly perod ll ge he smaller egh In hs saon e have: ran = ( sa ) ( ) ep ap ( ξ ca + ψaqa + ζcsa ' ) and ep are only assocaed h crren saon b ohers are assocaed h hsory For he condon ha only concern abo he hsory e have: ' ran = ( sa ) ( ) ep ap ( ξ ca + ψaqa ) III SYSTEM IMPLEMENTATION AND EVALUATION A Ml-agen Archecre A ell desgned archecre may ensre good sysem performance as ell as s flexbly and expansbly herefore pdae and ransfer of hs sysem can be mplemened on hgh effcency ST sysem ses mlagen archecre o acheve hs goal as fg3 Fg3 Ml-agen Archecre There ere en agens sed n hs sysem and formed he ml-agen archecre The fncons of each agen are as follongs: Agen_P: preprocessng agen for vsng sessons I processes he log fle colleced by sysem and forms a vsng vecor of sessons on adversemen Agen_P: preprocessng agen for hsory sessons I processes he log fle colleced by sysem and forms a vsng vecor of sessons on eb pages Agen_D: mnng agen for adversemen vsaon I analyzes vsng vecor of sessons on adversemen and forms a vsng paern Agen_D: mnng agen for eb page vsaon I analyzes vsng vecor of sessons on eb pages and forms a vsng paern Agen_M: monorng agen for sessons I deecs and manages sessons and sers behavors 009 ACADEMY PUBLISHER

6 JOURNAL OF COMPUTERS VOL 4 NO 8 AUGUST Agen_C: processng agen for negraon of nformaon I processes nformaon from legacy daa and exernal daa and negraes hem no he cenre daabase Agen_W: donloadng and reneng agen for eb pages I donloads and renes relave eb pages Agen_W: preprocessng agen for eb conens I exracs eyords and forms he eyord vecor of eb conens Agen_W3: mnng agen for eb conens I lzes he vecor of eb conens o form a clser of eyords and esablsh he concep space of eb conens Agen_R: processng agen for adversemen rle managemen I conrol adversng and argeng by adsmen of rles B Evalaon Tes We evalaed he fncons of hs sysem by a es sesson Ths sesson nclded 9 seps as n Fg4 Table provded s vsng pahay vsng paern neresed opcs dsplayng adversemens and he clcs on hese adversemens Table Process of Tes Sesson Frs he ser vsed homepage A he begnnng of hs sesson ST sysem fond he sole ser ID from hs cooes and mached hs vsng behavor h Paern No Accordng o he eyords of vsng eb pages ST sysem conned o machng hs neresed opcs h Topc No8 Ths s an eneranmen opc so he adversemen A from msc base and he adversemen B from Pod ere recommended o hs ser In he second sep he ser clced and as lned o a msc eb page He as sll mached h Paern No and Topc No8 adversemen C and adversemen D from he smlar eneranmen adversemen base ere recommended B n he hrd sep he vsed he eneranmen eb page hle hs vsng behavor paern as changed and mached h Paern No4 Ths s a paern of yong move fans so a famos move of adversemen D as recommended By sch-and-sch seps ST sysem had recommended 6 adversemens n hs sesson By machng and racng he vsng behavor paern and neresed opcs of sers ST sysem can mae a recommend sraegy a real-me To evalae he effcency and effecveness of ST sysem e adoped a real-es of 4 days We appled hs sysem o a real ebse Adversemens dsplayed on hs ebse ere recommended by manal operaon n he frs 8 days b by ST sysem n he las 6 days C Evalaon Indexes and Conrasve Resls The evalaon of onlne adversemen s dfferen from ha of radonal adversemen I ll consder he vsor s neracve response on specfed adversemen Fg5 olnes he ransformaon of adversemen effecs on Inerne Fg4 Tes Sesson and Adversemen Targeng Fg5 Transformaon of Adversemen Effecs 009 ACADEMY PUBLISHER

7 784 JOURNAL OF COMPUTERS VOL 4 NO 8 AUGUST 009 Adversng aracs vsors aenon and creaes s mpresson o hese vsors hereof leads o some clcs and frher behavors by he neresed vsors In hs process perceved mpresson and clced percen by vsors are ey facors o decde he ransformaon of adversemen effecs [5] A seres of ndexes as CP(X) are sally appled o evalae he onlne adversemen and decde s calclaon n cos In 995 InfoSee and Nescape frs sed he CPM n her onlne adversemen In 996 Yahoo and P&G presened CPC Hereafer Hoffman and Nova esablshed he exposre mercs and he neracvy mercs [6] Up o no CP(X) has been dely sed by Google and mos famos ebses as he represenave of neracvy mercs CP(X) ncldes some mporan ndexes as follongs: CPT-Cos Per Tme CPI-Cos Per Impresson CPM-Cos Per Monh Per Thosand Impresson CPC-Cos Per Clc CPS-Cos Per Sale CPA-Cos Per Acon In CPC an mporan ndex of CTR (Clc Throgh Rae) s sally appled o evalae he adversng effec: CTR = Clcs / Impressons In or evalaon he ell-non ndexes of CTR (Clc Throgh Rae) CPC (Cos Per Clc) and CPA (Cos Per Acon) ere adoped o gve he conrasve resls as Table and Table3 The conrasve resls of dynamc changes h 4 es days are Fg 6 and Fg7 Fg6 Change of CTR and CPC By smple analyss of sascs ST sysem has mproved he effcency and effecveness of adversemens In hs sysem any sensve daa of he sers are no reqred o be np so her prvacy s ell proeced Table Resls of Sascs Fg7 Change of Acons and CPA Table3 Sqares and F Tes From Table3 e can fnd ha he F parameers of CTR CPC and CPA are respecvely hle P <000 Ths ndcaes ha ST sysem has sgnfcan mpacs on recommendaon From Table e can fnd ha he average of CTR has ncreased 068% hle he CPC and CPA decreased 4836% and 485% respecvely IV SYSTEM APPLICATION AND PROSPECT The man moves for onlne adversng may be dfferen sch as brand promoon ncrease of vsors on ebse drec sales of prodcs and sppor servces for oher dsrbon [5] ST sysem can ads s argeng sraeges o sasfy he demands for dfferen moves ST sysem has he poenal prospec o be ell appled n he follong onlne adversng modes: () Por Adversng In hs mode onlne adversng s carred o on some ebse pors ST sysem can help ebse pors selec adversemen and mach onlne adversng o mos vsors behavors () Agency Adversng In hs mode he agency has sally ordered some specfed adversng schedles from ebses and shold decde he solon o maxmze hs profs ST sysem 009 ACADEMY PUBLISHER

8 JOURNAL OF COMPUTERS VOL 4 NO 8 AUGUST can help he agency mach schedles o an opmzed solon (3) Oner Adversng In hs mode onlne adversng s carred o on he oner s ebse ST sysem can help ebse mach adversemen o personalzed vsors by argeng echnqe V CONCLUSION Targeng echnqe s appled o mprove he effcency and effecveness of onlne adversng The aenon of crren argeng s ransformed from conen argeng freqency argeng me argeng and geographcal argeng o he exended argeng based on ser s characerscs h hgh effcency effecveness and proecon of prvacy Ths paper presened a ne smar argeng sysem for onlne adversng I ses Web conen mnng and Web sage mnng echnqes o rac and mne he ser behavors hdng n he hsorcal and crren ser sessons and desgns nerfaces for mananng he adversng rles hch can conrol he argeng sysem o aomacally delver he personalzed adversemens All he relaed noledge and rles are negraed by one nfed vecor space model The process of argeng doesn reqre any sensve daa np by sers so her prvacy s proeced Ths sysem has he poenal prospec o be ell appled n some onlne adversng modes sch as Por Adversng Agency Adversng and Oner Adversng Hoever hs sysem s o be mproved and perfeced n praccal applcaon Whle appled n large ebse he scale of vecors n paern recognon ll become hge so he opmzaon n daa processng and some fas algorhms are frher research or on hs sysem Complex adapve echnology and rles sch as ANN (Arfcal Neral Neor) echnology and mlobecve opmzaon rles are also sgnfcan o be explored n hs or ACKNOWLEDGMENT Ths research as sppored by Naonal Hgh-ech R & D Program (863 Program) of Chna (No008AA04Z7) Naonal Naral Scence Fondaon of Chna (No704000) and Shangha Leadng Academc Dscplne Proec (NoB0) REFERENCES [] Research Chna Onlne Adversng Research Repor hp://researchcomcn Mar8 009 [] Fn J and Kobsa A A reve and analyss of commercal ser modelng servers for personalzaon on he orld de eb User Modelng and User-Adaped Ineracon vol 0(3/4) pp [3] Allen C Kana D and Yaecel B Inerne World Gde o One-To-One Web Mareng Ne Yor: John Wley and Sons 998 [4] Aggaral C C Wolf J L and Y P S A frameor for he opmzng of WWW adversng LNCS vol 40 pp [5] Mobasher B Da H Lo T e al Effecve personalzaon based on assocaon rle dscovery from Web sage daa n Proceedngs of WIDM 00 Chang H L and Lm E P Eds Alana: ACM 00 pp9-5 [6] Mlan A Mnmal noledge anonymos ser proflng for personalzed servces LNAI vol 3533 pp [7] Mlan A Morc C and Neadoms R Fzzy machng of ser profles for a banner engne LNCS vol 3045 pp [8] Mlan A and Marcgn S An archecre for evolonary adapve eb sysems LNCS vol 388 pp [9] Zeng Chn Xng Chn-xao and Zho L-zh A srvey of personalzaon echnology Jornal of Sofare vol 3(0) pp Oc 00 [0] Yan YngWang Dalngand Y Ge Assocaon rles mnng algorhm of ebpage ves for personalzed recommendaon Comper Engneerng vol 3() pp 79-8 Jan 005 [] Zhang Bngq A collaborave flerng recommendaon algorhm based on doman noledge Comper Engneerng vol 3() pp7-0 Nov 005 [] W Lha and L L User proflng for personalzed recommendng sysems A reve Jornal of The Chna Socey For Scenfc and Techncal Informaon vol 5() pp 55-6 Jan 006 [3] Badsch P and Leopold D Aenon ndfference dsle acon: eb adversng nvolvng sers Nenomcs vol () pp [4] Jels A Targeed adversng and prvacy oo LNCS vol 00 pp [5] Berend B and Telzro M Addressng sers prvacy concerns for mprovng personalzaon qaly: oards an negraon of ser sdes and algorhm evalaon LNAI vol369 pp [6] Kazeno P and Adams M Personalzed eb adversng mehod LNCS vol337 pp [7] Hallam-Baer P M and Behlendorf B W3C orng draf hp://3org/pb/www/tr/wd-logfle hml Mar [8] Salon G Aomac Tex Processng-The Transformaon Analyss and Rereval of Informaon by Comper MA: Addson-Wesley 989 [9] Kazeno P Clserng of Hyperex Docmens Based on Flo Eqvalen Trees Wrocla: Wrocla Unversy of Technology 000 [0] Scharzopf E An adapve eb se for he UM00 conference n Proceedngs of he UM00 Gmyrasecz and Vassleva J Eds Berln Hedelberg: Sprnger-Verlag 00 pp77-86 [] Han J W and Kamber M Daa Mnng: Conceps and Technqes MA: Morgan Kafmann Pblshers 00 [] Zh T Usng marov chans for srcral ln predcon n adapve eb ses LNAI vol09 pp [3] Salon G Aomac Tex Processng-The Transformaon Analyss and Rereval of Informaon by Comper MA: Addson-Wesley 989 [4] Salon G Wong A Yang C S A vecor space model for aomac ndexng Commncaons of he ACM 975 8(5):63-60 [5] Sn Tao Research on User Behavor Targeng for Onlne Adversng [D] Shangha: Fdan Unversy 007 [6] Hoffman D L Nova T P Adversng prcng models for he World Wde Web n Inerne Pblshng and Beyond: he Economcs of Dgal Informaon and 009 ACADEMY PUBLISHER

9 786 JOURNAL OF COMPUTERS VOL 4 NO 8 AUGUST 009 Inellecal Propery Hrley D Kahn B and Varan H Eds Cambrdge UK: MIT Press 000 Da became a member of IEEE n 003 a senor member of Chna Comper Socey n 004 and a senor member of Chna Socey of Technology Economcs n 004 Weh Da receved hs BS degree n Aomaon Engneerng n 987 hs MS degree n Aomoble Elecroncs n 99 and hs PhD n Bomedcal Engneerng n996 all from Zheang Unversy Chna He s crrenly an Assocae Professor a he Deparmen of Informaon Managemen and Informaon Sysems School of Managemen Fdan Unversy Chna He has pblshed more han 00 ornal papers and conference arcles n Sofare Engneerng Informaon Sysems Servce Managemen Ne-ecology e-bsness ec Dr Xngyn Da receved her BS degree n Sofare Engneerng n 008 from Eas Chna Normal Unversy Chna She s crren a maser sden a School of Managemen Fdan Unversy Chna Tao Sn receved hs BS degree n Informaon Managemen and Informaon Sysems n 004 from Zheang Unversy Chna and hs MS degree n Informaon Managemen and Informaon Sysems n 007 from Fdan Unversy Chna 009 ACADEMY PUBLISHER