Web personalization based on static information and dynamic user behavior

ABSTRACT

The explosive growth of the web is at the basis of the great interest into web usage mining techniques in both commercial and research areas. In this paper, a web personalization strategy based on pattern recognition techniques is presented. This strategy takes into account both static information, by means of classical clustering algorithms, and dynamic behavior of a user, proposing a novel and effective re-classification algorithm. Experiments have been carried out in order to validate our approach and evaluate the proposed algorithm.

REFERENCES

• 1: Alex G. Büchner , Maurice D. Mulvenna, Discovering Internet marketing intelligence through online analytical web usage mining, ACM SIGMOD Record, v.27 n.4, p.54-61, Dec. 1998 [doi>10.1145/306101.306124]
• 2: Peter Cheeseman , John Stutz, Bayesian classification (AutoClass): theory and results, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
• 3: Data mining for path traversal patterns in a web environment, Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96), p.385, May 27-30, 1996
• 4: Magdalini Eirinaki , Michalis Vazirgiannis, Web mining for web personalization, ACM Transactions on Internet Technology (TOIT), v.3 n.1, p.1-27, February 2003 [doi>10.1145/643477.643478]
• 5: F. Grandi. An annotated bibliography on temporal and evolution aspects in the world wide web. TIMECENTER Technical Report TR-75, University of Bologna, Italy, September 2003.
• 6: Z. Huang. A cube model for web access sessions and cluster analysis. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.
• 7: L. Hubert and J. Schultz. Quadratic assignment as a general data-analysis strategy. British Journal of Mathematical and Statistical Psychologie, 29:190--241, 1976.
• 8: T. Joachims, D. Freitag, and T. Mitchell. Webwatcher: a tour guide for the world wide web. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages 770--777, August 1997.
• 9: H. Lieberman. Letizia: An agent that assists web browsing. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pages 924--929, 1995.
• 10: D. Mladenic. Machine learning used by personal WebWatcher. In Proceedings of the Workshop on Machine Learning and Intelligent Agents (ACAI-99), Chania, Greece, July 1999.
• 11: Maurice D. Mulvenna , Sarabjot S. Anand , Alex G. Büchner, Personalization on the Net using Web mining: introduction, Communications of the ACM, v.43 n.8, p.122-125, Aug. 2000 [doi>10.1145/345124.345165]
• 12: Mike Perkowitz , Oren Etzioni, Towards adaptive Web sites: conceptual framework and case study, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.31 n.11-16, p.1245-1258, May 17, 1999
• 13: Jaideep Srivastava , Robert Cooley , Mukund Deshpande , Pang-Ning Tan, Web usage mining: discovery and applications of usage patterns from Web data, ACM SIGKDD Explorations Newsletter, v.1 n.2, January 2000 [doi>10.1145/846183.846188]
• 14: K.-L. Wu , P. S. Yu , A. Ballman, SpeedTracer: a Web usage mining and analysis tool, IBM Systems Journal, v.37 n.1, p.89-105, 1997
• 15: L. Xu, A. Krzyzak, and E. Oja. Rival penalized competitive learning for clustering analysis, rbf net and curve detection. IEEE Transactions on Neural Networks, 4:636--649, 1993.
• 16: Tak Woon Yan , Matthew Jacobsen , Hector Garcia-Molina , Umeshwar Dayal, From user access patterns to dynamic hypertext linking, Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems, p.1007-1014, May 1996, Paris, France
• 17: Osmar R. Zaïane , Man Xin , Jiawei Han, Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs, Proceedings of the Advances in Digital Libraries Conference, p.19, April 22-24, 1998
• 18: F. Zhang and H.Y. Chang. Research and development in web usage mining system-key issues and proposed solutions: a survey. In Proceedings of the First IEEE International Conference on Machine Learning and Cybernetics, volume 2, pages 986--990, Beijing, November 2002.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms

General Terms:
Algorithms, Experimentation

Keywords:
clustering, web personalization, web usage mining