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180E-<strong>Commerce</strong>1million Group N mean SD F value Duncan.NBCFACMAH 260 0.693 0.187Non 5690 0.744 0.222L 90 1.228 0.314total 6040 0.749 0.231H 260 0.674 0.190Non 5690 0.727 0.218L 90 1.182 0.347total 6040 0.731 0.226*: p
Improving performance in recommender system:collaborative filtering algorithm and user’s rating pattern 181significant results from the prediction result of the error bound. This result is not theapproach of improving the prediction performance of algorithm, nor is the method ofdecreasing the prediction error, but it will be a useful basis for improving algorithms andalso understand users’ rating pattern better only by using already-existing ratings as preinformationbefore the prediction of users preferences about items.Also, we show the evaluation performances of classification functions which classify userswith lower or higher prediction accuracy before prediction processes using collaborativefiltering algorithm in the Recommender System. With our statistical analysis, we show thatapplying classification functions before prediction process to the users’ preference datawould get meaningful results for pre-evaluating users’ prediction accuracy. This isespecially useful to detect users who have lower prediction accuracy before time consumingprediction process. Additionally, it would be helpful to protect recommender system frommalicious attacker. However, this result also does not suggest the way to improve the userswho have been classified by proposed classification functions or make clear the reason whythese results are produced. It will be expected that further studies must be made in the nearfuture.In conclusion, it seems that in the near future, the field of recommender systems in e-business will attract even more interest from the research community. The increasingadoption of recommender system as a main tool for on-line marketing by prominentcompany and diverse field denotes its strategic role in on-line shopping environment.8. ReferencesAdomavicius, G. and Tuzhilin, A. (2005). Toward the Next Generation of RecommenderSystems: A Survey of the State-of-the-Art and Possible Extensions. IEEETransactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749, ISSN:1041-4347.Breese, J., Heckerman, D. and Kadie, C. (1998). Empirical Analysis of Predictive Algorithmsfor Collaborative Filtering, Proceedings of the 14th Annual Conference on Uncertainty inArtificial Intelligence, ISBN: 1-55860-555-X, pp. 43-52. July, San Francisco, CA.Condliff, M., Lewis, D., Madigan, D. and Posse, C. (1999). Bayesian Mixed-Effects Modelsfor Recommender Systems, ACM SIGIR '99 Workshop Recommender Systems:Algorithms and Evaluation, Aug.Han, U., Yang, G., Yoo, J., Chung, Y., Lee, H. (2008). A Proposal on the Error Bound ofCollaborative Filtering Recommender System, Proceeding of the 2nd InternationalConference on Multimedia and Ubiquitous Engineering, ISBN: 978-0-7695-3134-2, pp.348-354, Apl., Busan, Korea.Herlocker, J., Konstan, J., Terveen L. and Riedl, J. (2004). Evaluating collaborative filteringrecommender systems, ACM Transactions Information Systems, vol. 22, no. 1, pp.5-53,ISSN: 1046-8188.Lee, H., Lee, S., Chung, Y. (2007a). A Study on the Improved Collaborative FilteringAlgorithm for Recommender System, Proceeding of the 15th International Conferenceon Software Engineering Research, Management and Applications, ISBN: 0-7695-2867-8,pp. 297-304, Aug., Busan, South Korea
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IE-Commerce
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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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Consumer Responses to Colors of E-C
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120E-Commerce4. Research methodOur
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122E-Commerceavailable information
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124E-CommerceNewsvine Green. Starti
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126E-CommerceProcedureRespondents w
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128E-Commerce6. Data analysis and r
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A Mobile Commerce Model for Automob
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