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162E-<strong>Commerce</strong>Sinnappan S., Carlson J., (2004). An examination of website quality dimensions in Australiane-retailing: a confirmatory factor analysis approach, Intelligent InformationTechnology, Springer, Berlin/ Heidelberg, pp. 410-418Sommerville I. , (2007). Software Engineering 8. Addison-Wesley, 9780321313799, USA.Stefani A., Xenos M., (2008). E-<strong>Commerce</strong> System Quality Assessment Using A Model BasedOn ISO 9126 And Belief Networks, Software Quality Control, 16(1): pp. 107-129.Sutcliffe A. G., (2002). Assessing the reliability of heuristic evaluation for websiteattractiveness and usability, Proceedings of the 35th Hawaii InternationalConference on System Sciences, Honolulu: University of Hawaii, pp. 1838-1847.Stefani A., Xenos M., (2001). A model for assessing the quality of e-commerce systems,Proceedings of the PC-HCI, 2001, pp. 105-109.Sutcliffe A. G., (2001). Heuristic evaluation of website attractiveness and usability. in GISTTechnical Report G2001,1: Proceedings: 8th Workshop on Design, Specification andVerification of Interactive Systems, Glasgow, University of Glasgow, pp. 188-199.Tian J., (2005). Software Quality Engineering: Testing, Quality Assurance, and QuantifiableImprovment. Wiley Inter Science, CanadaZamzuri, N.H.A. Mohamed, N. Hussein, R., (2008). Antecedents of customer satisfactionin repurchase intention in the electronic commerce environment. Proceednig ofITSim 2008, Kuala Lumpor.
Improving performance in recommender system:collaborative filtering algorithm and user’s rating pattern 16310 XImproving performance in recommendersystem: collaborative filtering algorithm anduser’s rating patternHee Choon Lee, Seok Jun Lee and Young Jun ChungSang-ji University, Sang-ji University, Kangwon National UniversityKorea1. IntroductionWith the advance of IT technologies, a recommender system in online commerceenvironment has been introduced as personalized services (Schafer et al., 2006). Therecommender system is used in E-commerce for recommending a product, an item or evenany web service to each customer based on customer’s preference.Since a recommender system can predict customers’ preference and forecast the futuredegree of customer’s fondness for a certain item and services, it is used as a conspicuousservice which distinguishes an on-line commerce service from an off-line commerce service.In predicting each user’s preference, a recommender system essentially provides enoughinformation of items and users because it is able to predict the specific user’s preference fora target item and suggest the result to users.One of the classic recommender systems is a content-based filtering system which usestextual contents. In the recommender system for an on-line movie rental process, two typesof profiles are usually used; movie profile and customer profile. The movie profile describesa movie category, main actors, and performance movie. The customer profile is created withthe historical experienced textual information, which is stored in the system, of items orusers for seeking the best fits.This type of approach works well in initial systems, but there are some drawbacks forexpanding the scales of recommender systems due to the following reasons. First, there aredifficulties in converting features of all traded items into textual data. Additionally, if thenumber of traded items extremely increases, it is not easy to automatically convert all items’information into textual forms. Second, since content-based systems only recommend itemsbased on the past experience of user, it cannot help the user choose items for specific cases.This problem is called over-specification for recommendations.Such drawbacks can be eliminated by collaborative filtering recommender systems, whichuse relationships between users and items that can be represented on numerical scales (i.e.preference rating). This preference rating information can be collected from tracks of clientswho surf web and purchase items. Typically, such types of recommender systems utilizeneighbour users’ data, using a set of data that has similar characteristics for
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IE-Commerce
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Published by In-TehIn-TehOlajnica 1
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VIIWe wish to thank the reviewers o
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IXContentsPrefaceV1. A Conceptual F
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A Conceptual Framework and an Exten
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12E-CommerceThe Application Layer c
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14E-CommerceService description. Th
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16E-Commerceunderstanding about the
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18E-CommerceThis chapter contribute
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20E-CommercePorter M (2001). Strate
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22E-CommerceTo evaluate the e-comme
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24E-CommerceAa 2a a4m1 m 2 m 3m (
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26E-CommerceV v ij , i 1,2,...,
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28E-CommerceIndexFuzzy numberAltern
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30E-CommerceS. Mabuchi (1988). An a
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32E-Commercetechnology implementati
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34E-Commerceapplied to study an ind
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36E-Commerce PEOU of the public sec
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38E-CommerceNITC (National Informat
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40E-Commercewill describe the mecha
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42E-Commerce(2) The manner of commu
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44E-CommerceIf ADS is employed in a
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46E-Commerce4.2 Case studyThe detai
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48E-CommerceAs a high-speed develop
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50E-Commerce4.3.2 The architecture
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52E-Commerceone conclusion is that
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54E-Commerce
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56E-Commerceorganizations expose de
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58E-Commercerules. Such adapters ne
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60E-CommerceThe third level of inve
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62E-Commercecommon “language” h
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64E-Commercewell described in (Mül
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66E-Commerce3.3 Organizational Comp
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68E-Commercethe interaction of defi
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70E-CommerceAs a first step towards
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72E-Commerce5. Conclusion and Outlo
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74E-CommerceSchmid, B. F., Schroth,
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76E-CommerceThe following section p
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78E-Commerce(a) G cooc(b) G overlap
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80E-CommerceBetweenness centrality
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82E-Commerce4.1.1 Generating Networ
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84E-CommerceThe resultant value som
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86E-CommerceG shareholdershareholdi
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88E-Commerce17: Murata Noda Screen
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90E-Commerceand testing data in the
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92E-Commerce(c) Centrality-based ra
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Ranking Companies Based on Multiple
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Ranking Companies Based on Multiple
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Considering Culture in Designing We
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Electronic Commerce Readinessin Dev
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Can a Recommender System induce ser
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A Mobile Commerce Model for Automob
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The Automatic Attaching Function Ba
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