Abstract
With the advent of the Internet, there is a dramatic growth of data available on the World Wide Web. To reduce information overload and create customer loyalty, E-commerce businesses use Web Personalization, a significant tool that provides them with important competitive advantages. Despite the growing interest in personalized systems, it is difficult to implement such a system. This is because many business-critical issues must be considered before the appropriate personalization techniques can be identified. In this study, online businesses are classified into a number of categories. After that, personalization techniques that are used nowadays in E-commerce businesses are described. Finally, guidelines for selecting suitable personalization techniques for applications in each E-commerce business domain are proposed. The results of the study suggest that both customer-driven and business-driven personalization systems should be promoted on the site in order to increase customer satisfaction.
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References
Allen, C.: Personalization-Yesterday, Today and Tomorrow. Retrieved May 2000 from the World Wide Web: http://www.personalization.com/soapbox/columns/allen-column-1.asp
Dean, R.: Personalizing Your Web Site. Retrieved June 1998from the World Wide Web: http://www.builder.com/Business/Personal/ss00b.html
Goldberg, D., Nichols, D., Oki, B. M., Terry, D.: Using Collaborative Filtering to Weave and Information Tapestry. Communications of the ACM 35 (1992) 61–70
Herlocker, J. L., Konstan, J. A., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. Group Lens Research Project, Department of Computer Science and Engineering, University of Minnesota. Retrieved July 1999 from the World Wide Web: http://www.cs.umn.edu/Research/GroupLens/
IBM High-Volume Web Site: Web Site Personalization. Retrieved January 2000 from the World Wide Web: http://www-4.ibm.com/software/developer/library/personalization/index.html
Levin A.: Sorting through Personalization and Targeting. Fastwater Rapids: Volume 1.12. Retrieved January 1999 from the World Wide Web: http://www.fastwater.com/
Luedi, A. E.: Personalize or Perish. NetAcademy, The Mcminstitute University of St. Gallen, Switzerland. Retrieved July 1997 from the World Wide Web: http://www.netacademy.org/
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. The Proceedings of ACM CSCW’94 Conference on Computer Supported Collaborative Work (1994) 175–186
Shardanand, U., and Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. The Proceedings of ACM CHI’ 95, Conference on Human Factors in Computing Systems (1995) 210–217
Yu, P. S.: Data Mining and Personalization Technologies. Database Systems for Advanced Applications. The Proceedings of the 6th International Conference (1999) 6–13
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© 2001 Springer-Verlag Berlin Heidelberg
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Sae-Tang, S., Esichaikul, V. (2001). Web Personalization Techniques for E-commerce. In: Liu, J., Yuen, P.C., Li, Ch., Ng, J., Ishida, T. (eds) Active Media Technology. AMT 2001. Lecture Notes in Computer Science, vol 2252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45336-9_8
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DOI: https://doi.org/10.1007/3-540-45336-9_8
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