International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

A Web Recommendation System using Grid based Support Vector Machines

  • Jun, Sung-Hae (Department of Bioinformatics & Statistics, Cheongju University)
  • Published : 2007.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

Main goal of web recommendation system is to study how user behavior on a website can be predicted by analyzing web log data which contain the visited web pages. Many researches of the web recommendation system have been studied. To construct web recommendation system, web mining is needed. Especially, web usage analysis of web mining is a tool for recommendation model. In this paper, we propose web recommendation system using grid based support vector machines for improvement of web recommendation system. To verify the performance of our system, we make experiments using the data set from our web server.

Keywords

References

  1. R. Agrawal, T. Imielinski, A. Swami, 'Mining association rules between sets of items in large databases', Proceeding of the ACM SIGMOD International Conference on Management of Data, 1993
  2. R. Cooley, P. N. Tan, J. Srivastava, 'Discovery of interesting usage patterns from web data', Technical Report TR 99-022, University of Minnesota, 1999
  3. D. Fisher, K. Hildrum, J. Hong, M. Newman, M. Thomas, R. Vuduc, 'SWAMI: A Framework for Collaborative Filtering Algorithm Development and Evaluation', Proceeding of SIGIR 2000, ACM Press, 2000
  4. P. Giudici, Applied Data Mining, Wiley, 2003
  5. J. Han, M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2001
  6. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1999
  7. J. S. Jang, S. H. Jun, K. W. Oh, 'Fuzzy Web Usage Mining for User Modeling', International Journal of Fuzzy Logic and Intelligent Systems, vol. 2, no. 3, pp. 204-209, 2002 https://doi.org/10.5391/IJFIS.2002.2.3.204
  8. S. H. Jun, 'Web Usage Mining Using Support Vector Machine', Lecture Note in Computer Science, vol. 3512, pp. 349-356, 2005
  9. P. Kazienko, P. Kuzminska, 'The influence of indirect association rules on recommendation ranking lists Intelligent Systems Design and Applications', Proceedings of 5th International Conference on ISDA, pp. 482 - 487, 2005
  10. T. M. Mitchell, Machine Learning, McGraw-Hill, 1997
  11. P. Resnick, N. Lacovou, M. Suchak, P. Berfstrom, J. Riedl, 'GroupLens: An Open Architecture for collaborative filtering of Netnews', Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, 1994
  12. A. J. Smola, Regression estimation with support vector learning machines, Master's thesis, Technische University, 1996
  13. V. Vapnik, Statistical Learning Theory, John Wiley & Sons