NEURO-FUZZY INFERENCE SYSTEM FOR E-COMMERCE WEBSITE EVALUATION

Transcription

1 NEURO-FUZZY INFERENE SYSTEM FOR E-OMMERE WEBSITE EVALUATION Huan Lu, School of Software, Harbn Unversty of Scence and Technology, Harbn, hna Faculty of Appled Mathematcs and omputer Scence, Belarusan State Unversty, Mnsk, Belarus E-mal: Ths paper descrbes a method for E-commerce webste evaluaton. Fuzzy AHP and neural network were appled to solve the problem. The proposed system conssts of four components: herarchcal structure development for fuzzy analytc herarchy process (FAHP), weghts determnaton, data collecton, and decson makng. Keywords: E-commerce webste evaluaton; FAHP; Neural-fuzzy; Knowledge based system. Introducton The conventonal approaches to evaluate E-commerce webstes are smply based on ther knowledge experence, checklst method, analog approach, and regresson model. These approaches can only provde a set of systematc steps for problem solvng wthout consderng the relatonshps between the decson factors globally. Moreover, the ablty and experence of the analyst(s) may also nfluence sgnfcantly the outcome. Therefore, ths study ams to develop a combned method. It uses algorthm wth the fuzzy rule-based neural network and fuzzy analytc herarchy process (AHP) for evaluatng E-commerce webste. The proposed system conssts of four components: ) herarchcal structure development for fuzzy analytc herarchy process (fuzzy AHP), ) weghts determnaton, 3) data collecton, and 4) decson makng. In the frst component, the herarchcal structure of fuzzy AHP s formulated by revewng the related references and ntervewng the E-commerce experts. Then, n the second component, a questonnare survey s conducted to determne the weght of each evaluaton factor. Fnally, fuzzy rule-based neural network s appled to evaluate the result and gve fnal mprovng suggestons. Pogg [] desgned a WebQual system to assess E-commerce qualty, wth no nterests on quantty consderng; Mranda [] desgned a qualty evaluaton method, but t can not combne experts opnons; hu [3] presented the rankng of webstes from best to worst usng fuzzy logc, however could not know the absolute value of each webste; and Sudhamathy [4] developed an evaluaton nstrument for E-commerce webstes, whch was only user s satsfacton from the frst-tme buyer s vew and dscussed less about qualty analyss. In ths research, the ANN employs users' preferences determned va the modfed analytc herarchy process for the varous evaluaton factors to dentfy a small subset of good canddate locatons from the locaton database. Then the selected locatons are further evaluated through computer-medated group dscusson. Methodology The evaluaton factors should be selected on the bass of the related papers and knowledge of doman experts. The respectve data for these factors should be collected from the publshed government documents and on-the-spot measurements for qualtatve data. Then, these data are transformed to normal form for tranng feed forward neural network wth

2 EBP learnng algorthm. In the establshment of the ANN, data of locaton factors are used as the nputs of the neurons on the nput layer whle busness performance of the webste (. e. number of vstng customers per day) s appled as the output value of the neuron on the output layer. The detaled dscusson can be found n the followng. Herarchcal structure development for the factors The analytc herarchy process (AHP) s one of the extensvely used mult-crtera decson-makng (MDM) methods [5]. One of the man ts advantages s the relatve ease wth whch t handles multple crtera. The frst step of fuzzy AHP s to revew the related papers and ntervew the experts about the specfc doman n order to decompose the problem herarchcally. Weghts determnaton After the herarchcal structure has been establshed, a questonnare based on the proposed structure should be formulated. The man goal of the questonnare s to compare pars of element, or crtera, of each level wth respect to every element n the next hgher level. In [6, 7], a nne-pont scale s recommended. Fg.. The ntellgent decson support system for evaluatng E-commerce webste Lngustc varables A lngustc varable s a varable whose values are words or sentences n a natural or artfcal language. Here, we use ths knd of expresson to compare two buldng E-commerce webstes evaluaton crtera by fve basc lngustc terms, as absolutely mportant, very strongly mportant, essentally mportant, weakly mportant and equally mportant wth respect to a fuzzy fve level scale [8]. In ths paper, the computatonal technque s based on the followng fuzzy numbers defned by Mon et al. The use of lngustc varables s currently wdespread and the lngustc effect values of E-commerce webstes found n ths study are prmarly used to assess the lngustc ratngs gven by the evaluators. Rankng the fuzzy number The procedure of defuzzfcaton s to locate the Best Nonfuzzy Performance value (BNP). Methods of such defuzzfed fuzzy rankng generally nclude mean of maxmal (MOM), center of area (OA), and a-cut. To utlze the OA method to fnd out the BNP s a smple and practcal method, and there s no need to brng n the preferences of any evaluators, so t s used n ths study. The BNP value of the fuzzy number R can be found by the followng equaton: [( UR LR) + ( MR LR) ] BNP [, ]. (3) = + LR 3 n

3 Fuzzy analytc herarchy The procedure for determnng the evaluaton crtera weghts by FAHP can be summarzed as follows: Step. Assgn lngustc terms to the parwse comparsons by askng whch s the more mportant of each two elements/crtera, such as Г = [ j ] = n n n = n n n n n n (4), 3, 5, 7, 9 crteron s relatve mportance to crteron j; j = = j; where, 3, 5, 7, 9 crteron s relatve less mportance to crteron j. Step. To use geometrc mean technque to defne the fuzzy geometrc mean and fuzzy weghts of each crteron by Buckley [9] as follows: ( n r = a a an ), ( ) w = r r rn, (5) where a s fuzzy comparson value of crteron to crteron n, thus, s geometrc n r w mean of fuzzy comparson value of crteron to each crteron, s the fuzzy weght of the th crteron, can be ndcated by a TFN, w. Here, and stand = ( Lw, Mw, Uw) Lw Mw Uw for the lower, mddle and upper values of the fuzzy weght of the th crteron. Data collecton and manpulaton Once the weght of each factor has been determned, the correspondng data of each evaluaton factor should be collected n order to tran the neural network. Intutvely, ths can be done by revewng the statstcal data of webste and conductng actual nvestgatons. It s wthout doubt that these data should be transformed n order to ft the neural network's format. Decson makng The fuzzy rule based systems n another category of fuzzy neural systems are represented by network archtectures. Ths category uses a neural network learnng algorthm to adjust the IF-THEN rules [0]. The form of fuzzy producton rule has been lsted below: R : If x s A and x s A Then y = f = a 0 + a x + a x R : If x s A and x s A Then y = f = a 0 + a x + a x (6) where * =, and * = + + f (x, x ) = a 0 + a x + a x, f (x, x ) = a 0 + a x + a x and y * = * f + * f n n n

4 Fg. 3. Fuzzy rule-based system archtecture Ths paper takes the fuzzy producton rule that s combng wth producton rules and fuzzy mathematcs. The form of fuzzy producton rule has been lsted below: IF A ( t, f ) and A ( t, f ) and and A n ( t n, f n ) THEN B (, λ) (7) A, A,, A n are precondtons of the rule; t, t,, t n are confdence coeffcents of the precondtons; f, f,, f n are membershp functons of the precondtons of the rule; B s concluson (rule post-condton); λ s the threshold value of the rule, t s also the lmt of rule may be used, as n (7). The rule can be used f and only f the confdence coeffcent s greater than the threshold value. s the confdence coeffcent for the rules or the possblty of case occurrence. (0 < ) For example, the most top rule means IF t s bad and t s bad and t 3 s bad and t 4 s bad THEN producton s very_bad n full text expresson when used n assessng usablty. IF (t, bad) and (t, bad) and (t 3, bad) and (t 4, bad) THEN (S, Very_bad). (8) In addton, (R, U, D) s for the three standards (Relablty, Usablty, Desgn); and result s for the fnal fuzzy output of the whole assessment process. For example, the most top-left rule can be performed as (9), plus t means usablty, and IF usablty s bad and relablty s bad and desgn s bad THEN result s very_bad n full text expresson. IF (R, bad) and (U, bad) and (D, bad) THEN ( S, Very_bad). (9) oncluson The exstng methods for E-commerce webstes evaluaton are not sutable for enterprses. Thus, an effectve evaluaton procedure s essental to promote the decson qualty. Ths work examnes ths knowledge-based system and proposes a multple- crtera framework for E-commerce webstes evaluaton. To deal wth the qualtatve attrbutes n subjectve judgment, ths work employs FAHP to determne the weghts of decson crtera for each relatve expert representatve. Ths process enables decson makers to formalze and effectvely solve the complcated, multple-crtera and fuzzy percepton problem of most approprate E-commerce webstes evaluaton. Accordng to the characterstc and the basc prncples of the E-commerce system, the webste evaluaton s a MDM problem, whch s assocated wth a set of conflctng and non-commensurable crtera. ombnng the Fuzzy AHP and ANN, we propose a hybrd MDM method. Therefore, the proposed method s accurate, flexble and effcent.

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