Improvg webste performae for searh ege optmzato by usg a ew hybrd MDM model Ye-hag he Isttute of ha ad Asa-Paf Studes, Natoal Su Yat-se Uversty, awa, R.O.. tayler530259@gmal.om Yu-Sheg Lu Departmet of Busess Maagemet, Natoal Su Yat-se Uversty, awa, R.O.. yuseg@gmal.om ABSRA he purpose of ths study s to establsh a deso model of searh ege rakg for provdg admstrators to mprove the performaes of webstes for satsfyg the eeds of users. o probe to the terrelatoshp ad fluetal weghts amog rtera of SEO, ad evaluate gaps to aheve the asprato level of mplemetato real world, ths researh utlzes a ew hybrd MDM model, ludg deso-makg tral ad evaluato laboratory (DEMAEL), DEMAEL-based aalyt etwork proess (DANP), ad VlseKrterumska Optmzaa I Kompromso Resee (VIKOR). hs study vestgates the webstes of leadg teholog ompaes, ludg laser system, eergy-effet lghtg, ad otrol system. he empral fdgs dsover that the rtera of SEO possessed a self-effet relatoshp based o DEMAEL tehque. Aordg to the etwork relato map, the dmeso that admstrators should mprove frst whe mplemetg SEO s teral webste optmzato. I the sx rtera for evaluato, meta tags s the most sgfat rtero flueg searh ege rakg, followed by keywords ad webste desg. he evaluato of searh ege rakg reveals that webste laser system outperforms those eergy-effet lghtg ad otrol system, beomg the optmal example for admstrators of webstes to make webste hgh raked durg the tme that ths study s exeuted. Keyword: searh ege optmzato, searh ege rakg, multple rtera deso-makg, deso makg tral ad evaluato laboratory, DEMAEL-based aalytal etwork proess. ISS 423
INRODUION I moder tmes, oe of the ma searh eges s the tal step to searh for formato whe people make desos. Aordgly, for admstrators of webstes, the forward appearae of the searh results s farly mportat. However, t s extremely ompettve to make a webste appear o the foremost page (Dye, 2008). Searh ege optmzato (SEO) s the proedure to advae the rakg of webstes o searh eges for partular searhg terms by maagg omg lks ad haratersts of webstes (Malaga, 200). Nevertheless, admstrators of webstes are muh terested how to mprove the fators of SEO tur, the level of mportae of eah fator, ad redug the gaps to aheve asprato level uder the osderato of SEO s fators. osequetly, to provde the soluto to these ssues, the purpose of ths researh s to establsh a deso model of arryg out SEO for admstrators of webstes to mprove the performaes of webstes for satsfyg the users eeds. hs paper wll date, by the proposed ew hybrd MDM (multple rtera deso-makg) model, the pratal sequee of mplemetg SEO, the fluetal weghts of rtera, ad how to evaluate the gaps of a webste to reah asprato level. Am ad Emrouzead (20), aordg to metasearh ege, proposed a lear programmg mathematal model for optmzg the raked lst result. May prevous researhes assumed the rtera/fators for explorg problems were depedet ad lear; however, they are terdepedet ad feedbak real world. Moreover, there are lots of fators flueg SEO; hee, the otrbutos to admstrators of webstes are lmted. Other preedg researhes o SEO were maly foused o trodug SEO (Yalçı & Köse, 200) ad vestgatg fluetal fators of SEO (Bar-Ila et al., 2006; Dye, 2008; Xag & Gretzel, 200; Zhag & Dmtroff, 2005). Yet, the messages oveyed, for admstrators of webstes, are merely what fators have mpat o SEO ad whether the fluee were postve or egatve. herefore, these fdgs to ostrut a deso model of searh ege rakg have lttle otrbuto to t. I addto, researhes o the terrelatoshp ad fluetal weghts amog fators were adequate. o supplemet prevous fdgs o SEO for establshg a deso model of searh ege rakg for admstrators of webstes to mprove webste performae for ahevg the greatest beeft of teret marketg, ths study utlzes a ew hybrd MDM model omprsg DEMAEL (deso makg tral ad evaluato laboratory), DANP (DEMAEL-based aalytal etwork proess), ad VIKOR (VlseKrterumska Optmzaa I Kompromso Resee) for explorg searh ege rakg based o SEO. We reogze the rtera of SEO for buldg a deso model of searh ege rakg by revewg lteratures. Aordg to the 2 ISS 424
survey of experts, ths paper uses DEMAEL tehque to assess the terdepedet ad feedbak problems amog rtera to form a etwork relato map (NRM). By employg DANP to overome the problems of depedee ad feedbak amog rtera based o bas oept of ANP (Saaty, 996), the fluetal weghts of eah rtero for hgh rakg o the searh eges a be obtaed; subsequetly, we rak the data to detfy the mportat rtera. Evetually, VIKOR s adopted to evaluate the performae of webstes ad to redue gaps based o NRM for ahevg the asprato level. Publshed work oetg suh a ew hybrd MDM model wth mprovemet strategy of SEO s very few. hs study, to brdge the breah, utlzes a empral ase of teholog ompaes webstes awa, ad provdes the maagemet of searh ege rakg wth a valuable deso model to mprove webstes performaes. he remader of ths study s orgazed as follows: Seto 2, the rtera of SEO a be detfed based o lterature revew. I Seto 3, a ew hybrd MDM model for establshg a deso model of searh ege rakg s llustrated. Seto 4 reveals a empral study of mprovemet strategy for hgh searh ege rakg to demostrate the usefuless of proposed model. Fally, olusos ad remarks are preseted Seto 5. RIERIA OF SEO FOR EXPLORING SEARH ENGINE RANKING SEO s the proedure to mprove the rakg of webstes for partular searhg terms o searh eges by maagg omg lks ad haratersts of webstes (Malaga, 200). Based o past lteratures, the purpose of ths seto s to detfy the rtera of SEO s two ma dmesos: teral ad exteral webste optmzato (Yalçı & Köse, 200). Webste desg, meta tags, ad keywords, for teral webste optmzato, are eessary for the webste, page ames, photos, lks, otet texts every page ad styles that used for the ste map, RSS (really smple sydato) feeds, related texts, pages dfferet laguages. Besdes, for exteral webste optmzato, og webste to the ste gude, utlzg soal meda fators, ad employg lks from other optmzed webstes to the related webpage are luded (Yalçı & Köse, 200). O the dmeso of teral webste optmzato, Bar-Ila et al. (2006) metoed that webstes a obta hgh rakgs for spef searh terms wth spef searh eges by desgg ad redesgg webpages (ex. awa SEO I., www.ioutsourg.om.tw). I the related lteratures of meta tags, lots of researhers have proved that t a greatly mprove the searh effetveess by assoatg the results of multple searh eges the form of a metasearh eges (Am & Emrouzead, 20; Bar-Ila et al., 2006; Spk et al., 2006; Spoerr, 2007; 3 ISS 425
Vaugha, 2004). As for keywords, Zhag ad Dmtroff (2005)foud that by reasg the frequey of keywords the ttle, the full-text ad both the ttle ad full-text, webpage vsblty a be mproved. O the other dmeso of exteral webste optmzato, Zhag ad Dmtroff (2005) suggested that after the test webpages were ready, they were posted the publ doma so that searh eges ould rawl ad dex them for advag ther vsblty. I the assoated lteratures of soal meda, Xag ad Gretzel (200) dsovered that soal meda play a mportat role whe usg a searh ege. Wth respet to lkage, Zhag ad Dmtroff (2005) stated that webpages wth hgh hyperlk were regarded as more sgfat or fluetal tha those wth low hyperlk. herefore, t was take to aout by some searh ege rakg algorthms to let the searh results more oeted. Namely, webpages wth better hyperlk a get hgher rakg tha other pages (Dye, 2008). Aordg to SEO, two dmesos have mpat o searh ege rakg: () teral webste optmzato, ad (2) exteral webste optmzato. Moreover, by revewg lterature, teral webste optmzato s affeted by three rtera: webste desg, meta tags, ad keywords; exteral webste optmzato, o the other had, s affeted by three rtera: publ doma, soal meda, ad lkage, whh are all arraged able. Dmesos Iteral webste optmzato (D ) Exteral webste optmzato (D 2 ) able Explaato of rtera Evaluato Desrptos rtera Webste desg ( ) A olleto of ole otet omprsg applatos ad doumets Meta tags ( 2 ) A method for webmasters to supply searh eges wth formato about ther webstes Keywords ( 3 ) A term utlzed as a keyword to retreve doumets o a searh ege Ste gude ( 4 ) A huma-edted dretory of the Web Soal meda ( 5 ) he use of web-based ad moble tehologes for teratve dalogue Lkage ( 6 ) Lks from other optmzed webstes to the related webpage ESABLISHING A NEW HYBRID MDM MODEL FOR SEARH ENGINE RANKING MDM s utlzed to osder multple rtera smultaeously for provdg deso makers wth a valuable deso model to make the optmal desos (zeg & Huag, 20). hs study employs the tehque of DEMAEL to buld the etwork relato map (NRM) for problems of MDM. Subsequetly, by usg DANP the fluetal weghts of rtera of the struture a be obtaed. he method of VIKOR, evetually, s utlzed to evaluate ompromse rakg ad gaps of the 4 ISS 426
alteratves. hese prpal stages are luded the framework of the ew hybrd MDM model. ostrutg the NRM by DEMAEL o buld a NRM, the DEMAEL tehque was utlzed to explore the terdepedet ad feedbak problems amog rtera (he & zeg, 20; Fotela & Gabus, 976; Lu et al., press; Ou Yag et al., 2008). he method has bee used may felds, suh as portfolo seleto, desg serve, developmet strateges, kowledge maagemet, ad vedor seleto (Ho et al., 20; Huag et al., 20; L & zeg, 2009; zeg et al., 2007; Yag & zeg, 20). he DANP for alulatg rtera s fluetal weghts based o the NRM Saaty (996) developed ANP to solve problems wth depedee or feedbak betwee rtera. However, he et al. (20) addressed that the tradtoal survey questoare of ANP was too omplated ad hard to omprehed. hs researh, aordg to Ou Yag et al. (2008), adopts a ew method of DANP to oquer the obstruto of arryg out ANP for alulatg the fluee weghts based o the NRM of DEMAEL. alulatg gaps for mprovg the alteratves by VIKOR he ompromse rakg method (VIKOR) as oe applable tehque to mplemet wth MDM model was proposed to determe the ompromse soluto (Oprov, 998; Oprov & zeg, 2002; Oprov & zeg, 2003; Oprov & zeg, 2004; Oprov & zeg, 2007; zeg et al., 2002a; zeg et al., 2002b; zeg et al., 2005). I addto, the soluto s feasble for deso-makers, beause t supples a maxmum group utlty of the maorty, ad a maxmal gap of mmum dvduals of the oppoet. hs ew hybrd MDM model employs the DEMAEL ad DANP proesses Setos 3. ad 3.2 to obta the weghts of rtera wth depedee ad feedbak, ad utlzes VIKOR for resolvg the ompromse soluto. EMPIRIAL ASE USING SEMIONDUOR PORFOLIO AS AN EXAMPLE I ths seto, a empral study s exhbted to demostrate the applato of the preseted model for evaluatg ad seletg the best webste of searh ege rakg. Bakgroud ad problem desrptos Web users browse the few ad forward searhg results (Jase & Spk, 2005); therefore, the ssue of searh ege rakg for admstrators of webstes s very sgfat. awa Network Iformato eter (WNI) reported that the 5 ISS 427
populato of usg teret awa has exeeded 6.95 mllo (73.57% of populato), ad the testy of aessg teret omes out o top of the world. Lookg for formato by teret has bee a very mportat tool; however, t s a rtal top for admstrators of webstes to make webstes forward lsted o searh ege results the tmes of formato exploso. Moreover, there are umerous fators flueg searh ege rakg; therefore, t s a hard problem for admstrators of webstes to evaluate ad advae searh ege rakg. I order to assst admstrators of webstes omprehedg what the fators are, ths researh vestgates the rtera the experts perspetve o SEO ad establshes a deso model of searh ege rakg. Data olleto he experts wth spealty of SEO ad professoal kowledge of teret marketg are the obets of ths study, ludg osultats of SEO, sholars of omputer see, ad maagers of teret marketg. Moreover, the experees of experts are depted as follows: osultats of SEO are hghly sklled varous aspets of tehology ad web desg, sholars of omputer see are those who have the spealty of formato egeerg ad the experee of teahg formato tehology, ad maagers of teret marketg spealze marketg ad advertsg of webstes. Experts pot of vew o the rtera ad the performaes uder osderato of rtera of the webstes metoed below are aqured by tervewg ad fllg questoares. A amout of 5 obets osst of 5 osultats of SEO, 5 sholars of omputer see, ad 5 maagers of teret marketg. he qusto s mplemeted August 20. Furthermore, ths study takes samples from webstes of teholog ompaes ludg Laser ools & ehs orp. (LL, www.lttorp.om), AMKO SOLARA Lghtg o., Ltd. (AMKO, www.amko.om.tw), ad awa Jatek Eletros Ltd. (Jatek, www.atek.om.tw) for admstrators of webstes to mprove searh ege rakg. Estmatg the relatoshps amog SEO for establshg NRM DEMAEL tehque proposed Seto 3. wll be used to explore the terdepedet ad feedbak problems amog sx rtera summarzed by lteratures. At frst, the fluee matrx A for rtera s exhbted (see able 2). Seodly, the ormalzed fluee matrx K for rtera a the be derved by Eq. () (see able 3). hrdly, based o Eq. (3), the total fluee matrx for rtera ad dmesos s alulated (see able 4 ad able 5). Fally, the NRM of fluetal relatoshp s 6 ISS 428
bult by the vetor r ad d from the total fluee matrx (see able 6) as show Fgure. able 2 he tal fluee matrx A for rtera rtera 2 3 4 5 6 0.000 3.733 3.667 2.600.667.600 2 3.733 0.000 3.867 2.933.867.933 3 3.667 3.933 0.000 2.800.733.800 4 2.600 2.933 2.800 0.000.733.800 5.667.867.733.733 0.000 3.867 6.600.933.800.800 3.867 0.000 able 3 he ormalzed dret-fluee matrx K for rtera rtera 2 3 4 5 6 0.000 0.259 0.255 0.8 0.6 0. 2 0.259 0.000 0.269 0.204 0.30 0.34 3 0.255 0.273 0.000 0.94 0.20 0.25 4 0.8 0.204 0.94 0.000 0.20 0.25 5 0.6 0.30 0.20 0.20 0.000 0.269 6 0. 0.34 0.25 0.25 0.269 0.000 able 4 he total fluee matrx for rtera rtera 2 3 4 5 6.282.567.527.30.07.5 2.56.440.62.383.77.92 3.533.627.374.354.49.63 4.30.388.349.030.02.025 5.07.82.45.02 0.820.040 6.5.96.59.025.040 0.837 Note: 2 t t t 00% = 2.252% < 5%, where deotes the umber of sample ad t s the average fluee of rtero o. 7 ISS 429
able 5 he total fluee matrx D for dmesos Dmesos D D 2 D 3.524 0.94 D 2 0.940 8.84 able 6 he sum of fluees gve ad reeved o rtera Dmesos/rtera r d r +d r - d Iteral webste optmzato (D ) 24.465 24.464 48.928 0.00 Webste desg ( ) 7.899 7.899 5.797-0.00004 Meta tags ( 2 ) 8.365 8.399 6.764-0.03337 Keywords ( 3 ) 8.20 8.66 6.367 0.03427 Exteral webste optmzato (D 2 ) 9.782 9.782 39.564-0.00 Ste gude ( 4 ) 7.04 7.05 4.209-0.00022 Soal meda ( 5 ) 6.306 6.307 2.63-0.00032 Lkage ( 6 ) 6.37 6.37 2.742-0.0003 r - d 0.002 0.00 r - d D (48.928, 0.00), Exteral webste optmzato (, 2, 3 ) 0.04 0.03 0-0.03 0 (5.797, -0.00004), Webste desg 5 6 7 3 (6.367, 0.03427), Keywords r +d 0 0 30 40 50-0.04 r +d r - d 2 (6.764, -0.03337), Meta tags -0.00-0.002 D 2 (39.564, -0.00), Exteral webste optmzato ( 4, 5, 6 ) 0.00032 0.0003 0-0.0003-0.00032 0 6 (2.742, -0.0003), Lkage 5 (2.63, -0.00032), Soal meda 2.6 3.0 3.4 3.8 4.2 r +d 4 (4.209, -0.00022), Ste guld Fgure he NRM of fluetal relatoshps wth SEO. Fdg fluetal weghts of rtera by DANP Aordg to the ostruto of the fluee etwork based o DEMAEL 8 ISS 430
(see Fgure 2), ths study utlzes DANP to alulate the level of mportae (global weghts) of sx rtera show as able 7~9. he empral results reveal that experts are muh oered wth meta tags ad keywords, yet less oered wth lkage ad soal meda. he fdgs dsplay that the level of mportae s muh hgher meta tags, keywords, ad webste desg. oretely, A gas the hghest pot of 0.90, followed by keywords (0.85) ad webste desg (0.78). Moreover, the level of mportae of lkage ad soal meda s relatvely lower averagg 0.44. If omparso s made amog dmeso, experts regard meta tags as the most mportat rtero the dmeso of teral webste optmzato (D ). O the other had, ste gude s osdered by experts as the most sgfat rtero the dmeso of exteral webste optmzato (D 2 ). he fdg shows that experts suggest meta tags s the last rtero for admstrators of webstes should eglet whe mplemetg searh ege rakg. As far as dmesos are oered, experts are muh oered wth dmeso of teral webste optmzato (D ) beause the mea of t s muh hgher tha the other. I addto, the tegrated values are alulated to obta the total performae as preseted able 0. he results show the total performae s hghest webste of LL, followed by webstes of AMKO ad Jatek. osequetly, aordg to the deso model of searh ege rakg provded by ths paper, admstrators of webstes are suggested to take the webste of LL as a example whe advag searh ege rakg based o SEO. goal Searh ege optmzato dmesos rtera Iteral webste optmzato (D ) Outer-depedet Exteral webste optmzato (D 2 ) Webste desg ( ) Ste guld ( 4 ) Meta tags ( 2 ) Soal meda ( 5 ) Keywords ( 3 ) Ier-depedet Lkage ( 6 ) Ier-depedet alteratves Fgure 2 Webste of LL (A ) Webste of AMKO (A 2 ) Webste of Jatek (A 3 ) Aalyt framework of fluee etwork of SEO. able 7 he uweghted supermatrx based o DANP rtera 2 3 4 5 6 0.293 0.338 0.338 0.322 0.322 0.32 9 ISS 43
2 0.358 0.32 0.359 0.344 0.344 0.345 3 0.349 0.349 0.303 0.334 0.334 0.334 4 0.369 0.369 0.369 0.336 0.352 0.353 5 0.34 0.34 0.34 0.330 0.286 0.359 6 0.37 0.38 0.37 0.334 0.362 0.288 able 8 he weghted supermatrx rtera 2 3 4 5 6 0.62 0.87 0.87 0.78 0.78 0.77 2 0.98 0.73 0.98 0.90 0.90 0.9 3 0.93 0.93 0.68 0.85 0.85 0.85 4 0.65 0.65 0.65 0.50 0.57 0.58 5 0.40 0.40 0.40 0.48 0.28 0.60 6 0.42 0.42 0.42 0.49 0.62 0.29 able 9 he stable matrx of DANP whe power lmt z rtera 2 3 4 5 6 0.78 0.78 0.78 0.78 0.78 0.78 2 0.90 0.90 0.90 0.90 0.90 0.90 3 0.85 0.85 0.85 0.85 0.85 0.85 4 0.60 0.60 0.60 0.60 0.60 0.60 5 0.43 0.43 0.43 0.43 0.43 0.43 6 0.44 0.44 0.44 0.44 0.44 0.44 able 0 he weghts of rtera for evaluatg webste ad total performae by SAW Dmesos/ rtera Loal Weght Global Weght Webste of LL (A ) Webste of AMKO (A 2 ) Webste of Jatek (A 3 ) Iteral webste optmzato (D ) 0.553 7.784 7.89 7.087 Webste desg ( ) 0.322 0.78 (3) 8.867 9.33 8.600 Meta tags ( 2 ) 0.344 0.90 () 7.467 7.400 6.467 Keywords ( 3 ) 0.335 0.85 (2) 7.067 7.200 6.267 Exteral webste optmzato (D 2 ) 0.447 6.639 6.309 6.30 Ste gude ( 4 ) 0.358 0.60 (4) 4.33 3.933 2.533 Soal meda ( 5 ) 0.320 0.43 (6) 7.400 7.533 7.800 Lkage ( 6 ) 0.322 0.44 (5) 8.667 7.733 8.467 0 ISS 432
otal Performae 7.272 () 7.84 (2) 6.659 (3) Example: alulatg otal Performae by global weghts: 0.78*8.867+0.90*7.467+0.85*7.067+0.60*4.33+0.43*7.400+0.44*8.667=7.272 alulatg otal Performae by loal weghts: 0.553*7.784+0.447*6.639=7.272 ompromse Rakg by VIKOR After the weghts of rtera are obtaed by DANP Seto 4.4, VIKOR tehque s employed for ompromse rakg. he results of alulato (able ) llustrates that the total gap s hghest webste of Jatek, followed by webstes of AMKO ad LL. herefore, both VIKOR ad DANP ome to the same oluso that the deso model of searh ege rakg demostrates the webste of L s a optmal gude to be hgh raked o searh ege. able he relatve gaps from aspred value by VIKOR Dmesos/ rtera Loal Global Wegh Weght t Webste of LL (A ) Webste of AMKO ( A 2 ) Webste of Jatek (A 3 ) Iteral webste optmzato (D ) 0.553 0.222 0.2 0.29 Webste desg ( ) 0.322 0.78 (3) 0.3 0.087 0.40 Meta tags ( 2 ) 0.344 0.90 () 0.253 0.260 0.353 Keywords ( 3 ) 0.335 0.85 (2) 0.293 0.280 0.373 Exteral webste optmzato (D 2 ) 0.447 0.336 0.369 0.387 Ste gude ( 4 ) 0.358 0.60 (4) 0.587 0.607 0.747 Soal meda ( 5 ) 0.320 0.43 (6) 0.260 0.247 0.220 Lkage ( 6 ) 0.322 0.44 (5) 0.33 0.227 0.53 S A otal gaps Q A Maxmal gaps 0.273 () 0.282 (2) 0.334 (3) 0.587 () 0.607 (2) 0.747 (3) Example: alulatg dmeso gap by dmesos of loal weghts: 3 D D f f p D k 0 8.867 0 7.467 0 7.067 SD d 0.322 0.344 0.335 D w D D f f 0 0 0 0 0 0 =0.222 p Q d 0.293 D D alulatg total gap by rtera of global weghts: 6 * f f p A 0 8.867 0 7.467 0 7.067 SA d w f 0 0 0 0 0 0 f 0 4.33 0 7.400 0 8.667 0.60 0.43 0.44 0.273 0 0 0 0 0 0 A 0.78 0.90 0.85 * * f f p A QA d max,..., 0.587 A * f f ISS 433
IMPLIAIONS AND DISUSSIONS he empral fdgs are dsussed as follows. I the frst plae, NRM (Fgure ) of SEO ostruted by DEMAEL reveals that admstrators of webstes should mprove frst s teral webste optmzato (D ), f the searh ege rakg of webstes gog dow. Whe brastorm for the rght keywords ( 3 ) of spef felds, webste desg ( ) satsfes the eeds of users, ad meta tags ( 2 ) get sutable desrpto for searh eges, exteral webste optmzato (D 2 ) a get followg fluees: the ste gudes ( 4 ) of searh eges wll osder these webstes as sgfat webstes ad put them to dex; users of these webstes wll share valuable formato to ther freds by soal meda ( 5 ); other webstes wll make lkage ( 6 ) to these webstes for provdg users wth omplete formato. Seodly, the most sgfat rtero foud by DANP whe mplemetg SEO s meta tags ( 2 ), whh weghts 0.90. Whe t omes to searh ege rakg, a essetal proedure for admstrators of webstes to osder s that webstes should be foud by searh eges. If meta tags are ot desrbed properly, searh eges aot aess to the formato provded by webstes ludg vdeos, audos, ptures, webpages, ad so forth. herefore, admstrators of webstes should gve every formato approprate desrptos ot oly for searh ege to fd, but also let users easly look for the formato that they eed to make desos. hrdly, the fluetal weght of keywords ( 3 ) s 0.85 raked seod amog the sx rtera of SEO. Oe searh eges a fd the webstes, the ext ardal ssue for admstrators of webstes s to let users have the opportutes to searh for formato by utlzg keywords. May admstrators of webstes may set up keywords aordg to ther busesses; however, these webstes a be regarded as ot exsted by deso makers, f they do ot show up o the searh ege results by the deso makers keywords, though the busesses are operated well. herefore, admstrators of webstes should brastorm for the optmal keywords from the stadpot of deso makers to have ther webstes appeared o target users. At the last pot, the ompromse rakg by VIKOR reveals that the webste of LL (A ) s the optmal example amog the three teholog dustres for admstrators of webstes to mprove performaes of webstes for ahevg asprato level. he proposed ew hybrd MDM model based o SEO a be utlzed worldwde webstes. Admstrators of webstes a adust the fluetal weghts of the sx rtera aordg to the stuatos of dfferet outres to obta valuable formato of deso makg whe mprovg performaes of webstes. Moreover, they a selet the webstes of ther dustres to evaluate ad redue ther gaps for advag searh ege rakg. 2 ISS 434
ONLUSIONS AND REMARKS SEO s utlzed the feld of teret marketg as a sgfat referee for hgh rakg o searh eges. It has bee developed for deades ad examed that two dmesos of teral ad exteral webste optmzato have fluees o searh ege rakg. Nevertheless, t s ulear how the rtera mpat o the two dmesos. Although the ompreheso of the mportae of the rtera a be useful for admstrators of webstes whe mplemetg SEO, the weghts of rtera are seldom vestgated. By utlzg DEMAEL, the rtera are demostrated havg terrelatos ad self-feedbak relatoshps. Moreover, DANP s employed to obta weghts of the sx rtera. Empral fdgs show that meta tags s the most mportat rtero, followed by keywords, webste desg, ste gude, lkage, ad soal meda. Experts suggest that admstrators of webstes put the most emphass o meta tags, though they must omprehesvely take rtera to osderato whe makg desos of SEO. As for evaluatg SEO, the hghest tegrated sores of rtera s the webste of LL, followed by webstes of AMKO ad Jatek, ad the result s the same wth VIKOR method. herefore, experts date that SEO of LL s webste s a optmal example whe mplemetg SEO for provdg admstrators of webstes to aheve the greatest beeft of teret marketg. Preedg studes pay most atteto to trodug SEO ad detfyg the rtera that fluee t. However, lttle researhes are oered about the terrelatoshp amog rtera, the weghts of rtera, ad the evaluato of webste s SEO. hs paper thus proposes a ew hybrd MDM model ad vestgates the perspetves of experts for explorg these ssues. Assoatg prevous theoretal researhes wth the experts of pratal experee lets SEO more beefal for admstrators of webstes to mprove searh ege rakg of webstes, whh s ot offered by earler studes. I bref, ths researh utlzes a ew hybrd MDM model based o SEO to explore the subet for mprovg ad evaluatg searh ege rakg, ad further studes a osder more omprehesve fators or sub-fators to make ths feld more mature. Appedx A. A ew hybrd MDM model ombed wth DEMAEL, DANP ad VIKOR A.. DEMAEL he method s llustrated as follows: frst, we aqure the fluee matrx by sores. he experts are requred to pot out the degree of fluee amog rtera: datg rtero mpats o rtero as a. he fluee matrx, A, a thus be reeved. Seod, the ormalzed fluee matrx K a be alulated by ormalzg 3 ISS 435
A va Equatos () ad (2). K=m A () m m, max a max a hrdly, Derve the total fluee matrx. a be derved by usg the (2) formula, 2 3 q K + K K + K = K( I- K ), where I deotes the detty matrx. I the fourth step: ostrut the NRM based o the vetors r ad d. he vetors r ad d of matrx stad for the sums of rows ad olums respetvely, whh are show as Equatos (3) ad (4). r [ r ] t (3) d [ d ] t (4) where r represets the sum of the th row meag the total mpats of rtero o aother rtera. Also, d deotes the sum of the th olum of matrx ad dsplayg the etre effets that rtero reeves from aother rtera. Moreover, whe ( r d), t shows the degree of fluees gve ad reeved;.e., ( r d) exhbts the testy of the ardal role that fator performs the problem. Other fators are affeted by fator, whe ( r d ) s postve. Yet, f ( r d) s egatve, other fators mpat o fator ad thus the NRM a be bult (Lou et al., 2007; zeg et al., 2007). A.2. Based o DEMAEL tehque to fd ANP weghts DANP ossts of four steps (Lee et al., 20), ad the frst step s to buld the ostruto of the fluee etwork based o DEMAEL. I the seod step, the uweghted supermatrx s alulated. he total fluee matrx s derved from DEMAEL, as show Equato (5). D D D m m m D 2 m D 2 m D 2 m 4 (5) ISS 436
Use the total degree of fluee to ormalze every level of for aqurg based o Equato (6). D 2 m D 2 m 2 D m D D D m... m m (6) where α a be alulated va Equato (7) ad (8), ad we a obta α by the same way. m d t,,2,..., m (7) t / d t / d t / d t / d t / d t / d t / d t / d t / d m m m m m m m m m t t t t t t t t t m m m m m m (8) Aordg to the terdepedet relatoshp group to array, the uweghted supermatrx a the be obtaed by Equato (9). D 2 m ' D 2 2 D m D D D m... m m W W W W ( ) W W W m W W W (9) where W a be derved by Equato (0), ad so does W. Besdes, the group or rtero s depedet, f a blak spae or 0 shows up the matrx. 5 ISS 437
m m t t t m W ( ) t t t (0) m t t t m m m m he step 3 s to derve the weghted supermatrx. he total fluee matrx of dmesos D s outed by Equato (). Utlze the total degree of fluee to ormalze every level of D for obtag D aordg to Equato (2). d t,,2,..., D D = t t t D D D t t t D D D t t t D D D () D t / d t / d t / d t t t D D D D D D t / d t / d t / d 2 2 2 t t t D D D D D D / / / t d t d t d t t t D D D D D D (2) he weghted supermatrx a thus be alulated by ormalzg D to the uweghted supermatrx show Equato (3). t W t W t W W W W W W t W t W t W D D D D td td td D D D (3) I the fourth step, the lmt supermatrx s alulated. he weghted supermatrx multples by tself may tmes, based o the oept of Markov ha, to aqure the lmt supermatrx. herefore, the fluetal weghts of every rtero s alulated by 6 ISS 438
lm W z z. Spefally, by the lmt supermatrx W wth power z, deotg ay fgure for power, the fluetal weghts of DANP are aqured. Assume the alteratves are represeted by A, A2,..., Ak,..., A m. Also, f k deotes the performae sore of alteratve A k uder osderato of the th rtero; the weght (relatve mportae) of the th rtero s w, where,2,...,, ad s the umber of rtera. VIKOR beg wth the followg form of L p metr : p * * p / p k { [ ( k ) / ( )] }, L w f f f f where p ; k,2,..., m; weght w omes from DANP. VIKOR are thus utlzed to formulate the rakg ad gap measure p L k (as k S ) ad p L k (as Q k ). p * * k k k S L [ w ( f f ) / ( f f )] Q L f f f f. p * * k k max{( k ) /( ),2,, }, he ompromse soluto m L p k presets the mmum tegrated gap ad wll k be hose that ts value s the losest to the aspred level. Besdes, whe p s small (suh as p ), the group utlty s stressed; o the otrary, f p teds to be fte, the dvdual maxmal gap should be mprove pror to others, for t s muh mportat (Fremer & Yu, 976; Yu, 973). herefore, m S k aets the maxmum group k utlty; o the other had, m Q stresses o the seleto of the mmum gap from the maxmum dvdual gaps. k k REFERENES Am, G. R., & Emrouzead, A. (20). Optmzg searh eges results usg lear programmg. Expert Systems wth Applatos, 38(9), 534-537. Bar-Ila, J., Mat-Hassa, M., & Levee, M. (2006). Methods for omparg rakgs of searh ege results. omputer Networks, 50(0), 448 463. he,. H., & zeg, G. H. (20). reatg the aspred tellget assessmet systems for teahg materals. Expert Systems wth Applatos, 38(0), 268-279. 7 ISS 439
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