An Agent-based Online Shopping System in E-commerce

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1 Vol. 2, No. 4 Computer ad Iormatio Sciece A Aget-based Olie Shoppig System i E-commerce Zimig Zeg Ceter or Studies o Iormatio Resources, Wuha Uiversity, Wuha , Chia zmzeg977@63.com The paper is Supported by the MOE Proect o Key Research Istitute o Humaities ad Social Sciece i Chiese Uiversities (NO: 07JJD870220) Abstract The paper presets a aget-based shoppig system. First, the system ca acquire the customer s curret eeds rom system-customer iteractios. The the system itegrates built-i expert kowledge ad the customer s curret eeds, ad recommeds optimal products based o multi-attribute decisio method. I order to maitai a sematic coversatio with sellers, the commodity otology is also utilized to support sharable iormatio ormat ad represetatio. Fially, a experimetal prototype based o JADE is developed. Keywords: E-commerce, Aget, Multi-attribute decisio makig, Collaborative ilterig, Commodity otology. Itroductio The Iteret ad World Wide Web are becomig a importat chael or retail commerce as well as or busiess to busiess trasactios. It is udeiable that daily lie has become coveiet with olie shoppig. People do ot drive to a store, do ot travel to oversea, they ca purchase the commodities ad get the services they wat. Forrester research, Iteratioal Data Corp., ad Nielse Media Research have reported that the umber o people buyig, sellig ad perormig trasactios o the Web is icreasig at a pheomeal pace. At preset, however, the potetial o the Iteret or trasormig commerce is largely urealized. Electroic purchases are still largely o-automated. So the expoetially icreasig iormatio alog with the rapid expasio o the busiess websites causes the problem o iormatio overload. This o courses speds customers too much time o visitig loodig o retail shops o websites to kow about the commodities ad to survey the relevat commodity iormatio or urther compariso. Oe way to solve the above problem is to develop itelliget shoppig systems to provide persoalized iormatio services. The system ca iteract with customers ad capture what they eeds, so it provides decisio support or them to buy o the Web. Depedig o the types o commodities, dieret kids o shoppig systems should be developed to automate shoppig process by assistig customers to have commodity iormatio retrieval ad compariso i the massive iormatio eviromet o the Iteret. For the type o commodities that customers buy ote, such as ood, clothes ad books, the shoppig system ca be developed to acquire a customer s persoal preereces by aalysig his/her proile iormatio ad purchasig records(lee, J. Lee,J., Podlaseck, M., Schoberg, E., & Hoch, R. 200). For the commodities such as computers that a customer does ot buy ote, it is diicult to reaso about his/her previous preereces because there is ot eough iormatio available about his/her past purchasig record. I additio, the customer may have his/her speciic requiremets or each sigle shoppig ad have iadequate kowledge to evaluate the commodities. I order to automate shoppig process o this kid o commodities, the shoppig system i this paper is preseted, which ca provide cosultatio services ad decisio support via iterative iteractio with customers. Thereore, the system ca acquire ad aalyse a customer s curret eeds or preereces, the evaluates the cadidate commodities withi the database to recommed the optimal commodity or him/her. The paper is orgaized as ollows. I sectio 2, the shoppig process is described. I sectio 3, the itelliget shoppig system is implemeted based o the multi-attribute decisio makig method ad cosumer-based collaborative approach. Besides, the commodity otology is established i order to maitai a sematic coversatio betwee the system ad seller websites. Fially, the coclusios are draw i sectio The aalyse o the Itelliget Shoppig system Based o the aget techology, the shoppig system itegrates kowledge-based decisio-makig method ad 4

2 Computer ad Iormatio Sciece November, 2009 cosumer-based collaborative ilterig approach to provide decisio support or automatic shoppig. The shoppig process is list as ollowers ad its worklow is show. () First, the shoppig system perorms multiple sellers searchig task. The commodities ca be collected rom the sellers by a search egie ad stored i the iteral commodity database. (2) Ater the system gets all the commodities iormatio, it asks the customer aswer some qualitative questios to collect his/her eeds about the commodities. (3) Ater gatherig the customer s qualitative eeds, the system ca obtai the built-i expert kowledge to calculate the optimality o each commodity usig multi-attribute decisio makig method. (4) Oce the curretly available commodities have bee raked, the commodity with the top rak will be recommeded to the customer as the cadidate. (5) To speed up the shoppig process, a cosumer-based collaborative ilterig approach is used. The approach is based o the similar customer s purchasig record to provide more cadidate commodities or the curret customer. 3. The implemetatio o the shoppig system based o multi-aget 3. System ramework The overall goal here is to aalyse a customer s curret requiremets ad to id the most suitable commodity or him/her. To achieve the goal, the system cosists o ive types o agets that ca iteract with each other: iterace aget, buyer aget, expert aget, evaluatio aget ad collaboratio aget. These agets collaborate with each other by the message delivery mechaism ad make the whole system works together. The structure o the system is show i Figure. The detailed uctios o each aget i the shoppig system are described as ollows. ) Iterace aget The mai work o the iterace aget is bidirectioal commuicatio betwee the shoppig system ad customers. I order to collect ad aalyse the customer s curret eeds, the iterace aget asks him/her some specially desiged questios about the commodities. I the shoppig system, assumig that the customer does ot have eough domai kowledge to aswer quatitative questios regardig the techical details about the commodity, the system has to iquire some qualitative oes istead. For example, the system will ask the customer to express his eed o the display eature rather tha the basic requecy o CPU. 2) Buyer aget Buyer aget is a mobile aget, which ca migrate to the electroic marketplace ad search or the commodity iormatio rom multiple sellers. Whe it searches out oe seller, it will ask or oers about the commodity rom the respective seller. Ater the buyer aget gets all oers, it will retur back ad store the commodity iormatio i the iteral commodity database. I order to promote the eiciecy o searchig, it creates a group o child agets ad dispatches each to search or the oers o the commodity rom the respective seller. These child agets perorm parallel searchig, so buyer aget should supervise the ruig state o each child aget ad coordiate task distributio amog them. 3) Expert aget As is idicated, a importat issue i the desig o the system is how to use the expertise to provide the kowledge-based decisio support. The expert aget provides the commuicatio iterace with huma experts, by which the experts ca embed their persoal kowledge ito the system ad give a score o a commodity i each qualitative eed deied beore. With the expert aget, the system ca collects opiios rom dieret experts to give more obective suggestios. The the expert aget will covert them ito a specially desiged iteral orm or kowledge represetatio. However, huma experts seldom reach exactly the same coclusios. They may give dieret scores o the same commodity i the same qualitative eed sice their preereces are dieret. I order to resolve this problem, the system sythesizes all the expert s opiios ad assigs the same weights or them i the system implemetatio. I this way, the expert aget ca traser each commodity to a rak orm ad calculate its optimality accordigly. 4) Evaluatio aget The evaluatio aget is a importat compoet o the olie shoppig system. Ater receivig the oers o all commodities rom the sellers, the evaluatio aget will have compariso mechaism to evaluate each commodity i order to make the best possible selectio o all the supplied commodities. Sice shoppig is ot ust searchig or a lower price commodity. There is somethig else that should be take ito cosideratios like quality, reliability, brad, service, etc. I the system, the multi-attribute decisio makig method (Barbuceau, M., Lo, W. 2000)(Keeey, R. L., Raia, H. 993) is applied to evaluate commodities cosiderig multi-attributes o the commodities. Based o the multi-attribute evaluatio model, the evaluatio aget calculates the utility value o each commodity ad selects oe 5

3 Vol. 2, No. 4 Computer ad Iormatio Sciece that has maximal utility value as the recommeded commodity. Its mathematical model ca be described: Supposig C = c, c,, c } as the vector o the commodities iormatio that has bee gathered o Iteret, { 2 m A = a, a,, a } as the qualitative eature vector o the commodities, the utility value o the commodity { 2 c i ( i m) about the attribute a ( ) ca be deoted as i = ( c i ), which represets the relative perormace o the commodity c i the qualitative eature i. Thereore, the decisio matrix that cosists o m i ca be deoted as: F = 2 m 2 22 m2 2 m = ( ) () i m I order to acilitate mutual reerece betwee the multi- attributes easily, the decisio matrix should be ormalized, which ca be ollowed by ormula (2): ' i = m i= i ( ) Ater ormalizig the decisio matrix, the value o ' is limited i [0,]. The the evaluatio aget ca calculate the i utility value o each commodity based o the ormula (3). ( i U i ' ( c i ) i = 2 = ω (3) I the ormula (3), U c ) is the utility value o the commodity ( i m) ω is the weight o the qualitative c i. eature ( ), which meas the customer s curret requiremet i this qualitative eature ad ω = (2) =. Ater calculatig the utility o all the commodities, the evaluatio aget will select oe that has maximal utility value as the recommeded commodity. Fially, the evaluatio aget submits the recommeded commodity to the customer via iterace aget. The whole computig process is perormed by the evaluatio aget automatically. 5) Collaboratio aget As idicated beore, the user-system iteractio is a importat actor i achievig optimal recommedatio. Durig the iteractio, the cosumer ca give more eedback to the system by updatig his/her curret eeds util the cosumer is satisied with the shoppig result. However, the requet user-system iteractios ievitably take time. I the system, collaboratio aget is desiged to reduce the time o user-system iteractio. The collaboratio aget is based o the cosumer-based collaboratio approach(zeg Chu, Xig Chu-Xiao et al. 2004), which irst compares the eed patter o the curret customer to the oes previously recorded ad the system recommeds the commodities selected by the similar cosumers to the curret customer. The qualitative eed patter o a customer ca be deied as a vector W = ( ω, ω2,, ω ), i which ω i ( i ) meas the preerece score o customer s qualitative eed i the eature dimesio i, ad is the umber o qualitative eed eature. The collaboratio ca acquire the eed patter o previous customers easily by accessig the web log database o the system as show i table. The collaboratio aget uses the correlatio coeiciet o Pearso, which compares the curret customer s eed patter with the oes o the previous customers, ad the calculate the similarities betwee the curret customer ad all the previous customers. Its mathematic model ca be expressed as ollows: Supposig the eed patter o a curret customer a as the vector: Wa = ( ωa,, ωa,2,, ωa, ), the eed patter o a previous customer b as the vector: Wb = ( ωb,, ωb,2,, ωb, ). So the similarities Sim( a, b) betwee two eed patter ca be calculated as ormula (4): Sim( a, b) = = = ( ω ( ω a, a, ω )( ω ω ) a 2 a = b, ( ω ω ) b, b ω ) b 2 (4) I the ormula (4), ω a, ad ωb, represet the preerece score o qualitative eed eature that the curret 6

4 Computer ad Iormatio Sciece November, 2009 customer a ad previous customer b give respectively, while ω a ad ω b represet the average score o all the eatures that the curret customer a ad previous customer b give respectively. Through the similarity calculatio o qualitative eed eatures, the collaboratio aget ca search out the most similar eed patter or the curret customer rom the web log database. The system the predicts that what the curret customer is targetig may be the commodities that most similar previous customer ially purchased. Hece, the system also recommeds commodities derived orm the collaborative ilterig approach described above to the curret customer, i additio to the optimal commodity provided by the evaluatio aget. I this way, the system gives the customer more choice space ad a customer ca share experieces rom previous customers. O the other had, the umber o iteratios o user-system iteractio ca thus be reduced, ad the system ca work eve more eicietly. 3.2 Commodity otology The shoppig system should gather commodities iormatio rom multiple sellers, however, it is diicult to exchage iormatio betwee the shoppig system ad the sellers because o the dieret commodity data ormat i database ad represetatio. I order to maitai a sematic coversatio betwee the shoppig system ad sellers, there should be a commo laguage to support shared data ormat ad represetatio about the commodities iormatio. This is established by meas o a otology, which cotais the mai cocepts owig to the domai we are dealig with. I additio to this iormatio, the otology also icludes attributes, values, relatios betwee cocepts ad axioms so that cosistecy checkig ad iereces are doe (Ya H., Schreiber G., et al. 997). Thereore, the mai otological etity i the prototype system developed i the work is the cocept, but the use o other otological etities such as attributes is also possible i the model i order to provide the system with powerul represetatio capabilities. I this example, commodity otology show how a computer is composed by several elemets: moitor, keyboard, mouse, processor, etc, which ca be described as ollows usig OWL laguages (Deborah LM, Frak VH. 2004): <owl:class rd:id= computer > <rd: subclasso rd:resource= #Product /> <rd:subclasso> <owl:restrictio> <owl:oproperty rd:resource= hastrademark /> <owl: hasvalue rd:resource= #IBM /> </owl:restrictio> </rd:subclasso> <rd:subclasso> <owl:restrictio> <owl:oproperty rd:resource= #hasmodel /> <owl: cardiality rd:datatype= &xsd;onegativeiteger > </owl:cardiality> <owl: hasvalue rd:resource= #CompaqEvoD220 /> </owl:restrictio> </rd:subclasso> <rd:subclasso> <owl:restrictio> <owl:oproperty rd:resource= #hasguaraty /> <owl: cardiality rd:datatype= &xsd;onegativeiteger > </owl:cardiality> <owl: hasvalue rd:resource= 24/> </owl:restrictio> </rd:subclasso> 7

5 Vol. 2, No. 4 Computer ad Iormatio Sciece <rd:subclasso> <owl:restrictio> <owl:oproperty rd:resource= #hasorderprice /> <owl: micardiality rd:datatype= &xsd;onegativeiteger > </owl:micardiality> <owl: hasvalue rd:resource= /> </owl:restrictio> </rd:subclass> </owl:class> I this case, i additio to the cocepts takig part i the sematic relatio uder questio, the relatio will have a ame with the relatio type ad evetually some other properties associated to that relatio. 3.3 Web applicatio With the purpose o applyig itelliget agets to the e-commerce system, JADE platorm should be itegrated ito the Web applicatio. At irst, the eviromet iitializatio is eeded i order to start workig with JADE. This process ca be implemeted by AgetLoader, which reads coiguratio iles ad creates the AMS ad DF agets. The AMS ad DF agets provide white/yellow pages services respectively. O the oe had, DF provides a yellow pages service to the other agets i the system, which executes the tasks o aget registratio ad lookup. Whe the buyer or seller aget is created, it should be registered i AMS. O the other had, DF is resposible or moitorig the lie cycle o each aget ad tracig the behaviour o it. I this way, all the agets i the system ca be eectively maaged ad their iter-commuicatio will be acilitated well. The desig o the system is based o the Apache Struts Web Applicatio Framework ad ca be implemeted with Java techology. 4. Coclusios I this paper, I have idicated the eed to automate shoppig process o Iteret ad provide more persoalized iormatio services or customers. Thereore, developig itelliget shoppig system is a promisig way to achieve this goal. I the work, I preset a multi-aget system to provide shoppig service or the commodities that a cosumer does ot buy requetly. The system itegrates built-i expert kowledge ad the customer s curret eeds, ad recommeds optimal products based o multi-attribute decisio makig method. To reduce the eort o system-customer iteractios, the system utilizes customer-based collaboratio ilterig approach to recommed the products. Besides, i order to maitai a sematic coversatio with sellers, the commodity otology is also utilized to support sharable iormatio ormat ad represetatio. A prototype o the system is implemeted usig the Java Aget Developmet Framework (JADE). The result shows that the system perorms eicietly ad ca help customers save eormous time or Iteret shoppig. My uture work will be ocused o developig some security mechaisms to provide security services or the system. Reereces Barbuceau, M., Lo, W. (2000). A Multi-Attribute Utility Theoretic Negotiatio Architecture or Electroic Commerce, Proc. 4 th It. Co. O Autoomous Aget, Deborah LM, Frak VH. (2004). Owl web otology laguage overview, Staord Uiversity, USA. URL: Keeey, R. L., Raia, H. (993). Decisio with Multiple Obectives, Cambridge Uiversity Press, 993. Lee, J. Lee,J., Podlaseck, M., Schoberg, E., & Hoch, R. (200). Visualizatio ad aalysis o clickstream data o olie stores or uderstadig web merchadisig[j]. Data Miig ad Kowledge Discovery, 200, 5(-2): Ya H., Schreiber G., et al. (997). Usig explicit otologies i KBS developmet[j]. Iteratioal Joural o Huma Computer studies, 997, 45(2-3): Zeg Chu, Xig Chu-Xiao et al. (2004). Similarity Measure ad Istace Selectio or Collaborative Filterig [J]. Iteratioal Joural o Electroic Commerce, 2004, 8(4):

6 Computer ad Iormatio Sciece November, 2009 Figure. The architecture o the shoppig system 9