RSCTC 2002: Rough Sets and Current Trends in Computing pp 506-513 | Cite as
PagePrompter: An Intelligent Web Agent Created Using Data Mining Techniques
Abstract
Some challenges for Website designers are to provide correct and useful information to individual users with different backgrounds and interests, as well as to increase user satisfaction. Intelligent Web agents offer a potential solution to meet such challenges. A Web agent collects information, discovers knowledge through Web mining and users’ behavior analysis, and applies the discovered knowledge to give dynamically recommendations to Website users, to update Web pages, and to provide suggestions to Website designers. The basic functionalities and components of an intelligent Web agent are discussed. A prototype system, called PagePrompter, is described. The knowledge of the system is extracted based on a combination of Web usage mining and machine learning.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Agrawal, R., Imielinski, T. and Swami, A. Mining association rules between sets of items in large databases, Proceedings of SIGMOD, 207–216. 1993.Google Scholar
- 2.Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P. and Uthurusamy, R. Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996.Google Scholar
- 3.Florescu, D., Levy, A.Y. and Mendelzon, A.O. Database techniques for the World-Wide Web: a survey, SIGMOD Record, 27, 59–74, 1998.CrossRefGoogle Scholar
- 4.Hartigan, J. Clustering Algorithms, John Wiley, New York, 1975.MATHGoogle Scholar
- 5.Joachims, T., Freitag, D. and Mitchell, T.M. WebWatcher: a tour guide for the World Wide Web, Proceedings of the 15th International Joint Conference on Artificial Intelligence, 770–777, 1997.Google Scholar
- 6.Madria, S.K., Bhowmick, S.S., Ng, W.K. and Lim, E. Research issues in Web data mining, Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery, 303–312, 1999.Google Scholar
- 7.Mobasher, B., Jain, N., Han, J. and Srivastava, J. Web mining: pattern discovery from World Wide Web transactions, Proceedings of International Conference on Tools with Artificial Intelligence, 558–567, 1997.Google Scholar
- 8.Ngu, D.S.W. and Wu, X. SiteHelper: a localized agent that helps incremental exploration of the World Wide Web, Proceedings of 6th International World Wide Web Conference, 1249–1255, 1997.Google Scholar
- 9.Quinlan, J.R. C4.5: Programs for Machine Learning, Morgan Kaufmann. San Mateo, 1993.Google Scholar
- 10.Srivastava, J., Cooley, R., Deshpande, M. and Tan, P.N. Web usage mining: discovery and applications of usage patterns from Web data, SIGKDD Explorations, 1, 12–23, 2000.CrossRefGoogle Scholar
- 11.Wang, X.W. PagePrompter: An Intelligent Agent for Web Navigation Created Using Data Mining Techniques, M.Sc. Thesis, Department of Computer Science, University of Regina, 2001.Google Scholar
- 12.Yan, T.W., Jacobsen, M., Garcia-Molina, H. and Dayal, U. From user access patterns to dynamic hypertext linking, Proceedings of the 5th International World Wide Web Conference, 1007–1014, 1996.Google Scholar
- 13.Yao, Y.Y., Hamilton, H.J. and Wang, X.W. PagePrompter: An Intelligent Agent for Web Navigation by Using Data Mining Techniques, Technical Report, TR 2000-08, 2000, http://www.cs.uregina.ca/Research/2000-08.doc.
- 14.Yao, Y.Y., Zhao, Y. and Maguire, R.B. Explanation oriented association mining by combining unsupervised and supervised learning algorithms, Manuscript, 2002.Google Scholar