Application of back-propagation neural network for e-commerce customerspatterning

ICIC Express Letters

Volumn 3, Issue 3, 2009, Pages 775-785

  Chou, Pao Hua ^a,b   Hsu, Chao Hsing ^b   Wu, Chi Fei ^b   Li, Pi Hsiang ^a   Wu, Menq Jiun ^a  

^a NATIONAL CHANGHUA UNIVERSITY OF EDUCATION   (Taiwan)

^b CHIENKUO TECHNOLOGY UNIVERSITY   (Taiwan)

Author keywords

Artificial neural network; Automatic service; Back propagationnetwork; Data mining; Electronic commerce; Footstep graph

Indexed keywords

ANALYZING SYSTEM; ARTIFICIAL NEURAL NETWORK; AUTOMATIC SERVICE; BACK PROPAGATION NEURAL NETWORKS; BACK-PROPAGATIONNETWORK; BUSINESS CASE ANALYSIS; E-COMMERCE; HYBRID FRAMEWORK; INDIVIDUAL CUSTOMERS; NAVIGATION BEHAVIOR; SEQUENCE MINING; SPECIFIC KNOWLEDGE;

CUSTOMER SATISFACTION; DATA FLOW ANALYSIS; DATA MINING; ELECTRONIC COMMERCE; NAVIGATION;

NEURAL NETWORKS;

EID: 77953889806     PISSN: 1881803X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. Y. Li, and N. Zhong, Web mining model and its applications forinformation gathering, Knowledge-Based Systems, Vol. 17, No. 3, pp. 207-217,2004.
2. C. Allen, D. Kama and B. Yaeckel, Internet World Guide to One-toone WebMarketing, New York, Wiley, 1998.
3. S. S. Weng and M. J. Liu, Feature-based recommendations for one-to-onemarketing, Expert Systems with Applications, Vol. 26, No. 4, pp. 493-508, 2004.
4. W. P. Lee, C. H. Liu and C. C. Lu, Intelligent agent-based systems forpersonalized recommendations in internet commerce. Expert Systems withApplications, Vol. 22, No. 4, pp. 275-284, 2002.
5. J. R. Bettman and C. W. Park, Effects of prior knowledge and experienceand phase of the choice process on consumer decision processes: A protocolanalysis, Journal of Consumer Research, Vol. 7, pp. 234-248, 1980.
6. L. W. Alba, The effects of product knowledge on the comprehension, inRetention, and Evaluation of Product Information, in Advances in ConsumerResearch, R. P. Bagozzi and A. M. Tybout (eds.), Ann Arbor, MI: Association forConsumer Research, Vol. 10, pp. 577-580, 1983.
7. J. Jacoby, T. Troutman, A. Kuss and D. Mazursky, Experience and expertisein complex decision making, Advances in Consumer Research, R. J. Lutz (ed.),Provo, UT: Association for Consumer Research, Vol. 13, pp. 469-475, 1986.
8. S. Sen, Knowledge, information mode and the attraction effect, Journal ofConsumer Research, Vol. 25, pp. 64-77, 1998.
9. M. Drucks, The effect of product class knowledge on information searchbehavior, Journal of Consumer Research, Vol. 12, No. 1, pp. 1-16, 1985.
10. J. Han, and M. Kamber, Data Mining: Concepts and Techniques, Los Altos,CA: Morgan Kaufmann, 2001.
11. C. Kleissner, Data mining for the enterprise, Proc. of the 31stInternational Conference on System Science, Hawaii, pp. 295-304, 1998.
12. Attar Software Limited, XpertRuler Miner: Knowledge from Data, AttarSoftware Limited Inc, 2002.
13. G. Piatetsky-Shapiro, Discovery, analysis and presentation of strongrules, Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W. J. Frawley(eds.), AAAI/MIT Press, Cambridge, MA, 1991.
14. R. Agrawal, Imielinski and T. A. Swami, Mining association rules betweensets of items in large databases, Proc. of the SIGMOD Conference, pp. 207-216,1993.
15. S. Brin, R. Motwani, J. D. Ullman and S. Tsur, Dynamic item set countingand implication rules, Proc. of the International ACM SIGMOD Conference, pp.255-264, 1997.
16. P. Dömel, WebMap-a graphical hypertext navigation tool, Proc. of theSecond International WWW Conference, Chicago, USA, 1994.
17. J. Cugini, and J. Scholtz, YISIP: 3 D visualization of paths throughwebsites, Proc. of the International Workshop on Web-Based InformationVisualization, Florence, Italy, pp. 259-263, 1999.
18. F. J. Hirsch, S. Meeks and C. L. Brooks, Creating custom graphical webviews based on user browsing history. Sixth International World Wide WebConference, Santa Clam, A Poster Presentation, California, USA.
19. I. H. Ting, C. Kimble and D. Kudenko, A pattern restore method forrestoring missing patterns in server side click-stream data, in APWeb, Y. Zhanget al. (eds.), Springer-Verlag, LNCS, Vol. 3399, pp. 501-512, 2005.
20. D. Wu, Z. Yang and L. Liang, Using DEA-neural network approach toevaluate branch efficiency of a large Canadian bank, Expert Systems withApplications, Vol. 31, pp. 108-115, 2006.
21. A. Jost, Neural networks: A logical progression in credit and marketingdecision system, Credit World, Vol. 81, No. 4, pp. 26-33, 1993.
22. P. Werbos, Beyond Regression: New Tools for Prediction and Analysis inthe Behavioral Science, Ph.D. Dissertation, Committee on Applied Mathematics,Harvard University, Cambridge, MA, 1974.
23. J. W. Ronald, D. E. Rumelhart and G. E. Hinton, Learning internalrepresentations by error propagation, in Parallel Distributed Processing:Explorations in the Microstructure of Cognition, D. E. Rumelhart and J. A.McClelland (eds.), Vol. 1, Cambridge: MIT Press/Bradford Books, 1986.
24. L. Medsker, and J. Liebowitz, Design and, Development of Expert Systemsand, Neural Networks, New York, Macmillan, 1994.
25. S. S. Panda, D. Chakraborty and S. K. Pal, Flank wear prediction indrilling using back propagation neural network and radial basis functionnetwork, Applied Soft Computing, DOI: 10.1016/j.asoc.2007.07.003, 2007.
26. C. F. Tsai and J. W. Wu, Using neural network ensembles for bankruptcyprediction and credit scoring, Expert Systems with Applications, Vol. 34, No. 4,pp. 2639-2649, 2008.
27. C. J. Huang, D. X. Yang and Y. T. Chuang, Application of wrapper approachand composite classifier to the stock trend prediction. Expert Systems withApplications, Vol. 34, No. 4, pp. 2870-2878, 2008.
28. R. Cooley, B. Mobasher and J. Srivastava, Data preparation for miningworld wide web navigation patterns, Journal of Knowledge and Information System,Vol. 1, No. 1, pp. 5-32, 1999.
29. R. Zhou, S. C. Hui and A. C. M. Fong, An efficient approach for miningperiodic sequential access patterns, PRICAI 2004, LNAI 3157, C. Zhang, et al.(eds.), Springer-Verlag, pp. 485-494, 2004.