Browse > Article
http://dx.doi.org/10.5391/IJFIS.2005.5.1.088

User modeling based on fuzzy category and interest for web usage mining  

Lee, Si-Hun (School of ICE, Dept of ECE Sungkyunkwan University)
Lee, Jee-Hyong (School of ICE, Dept of ECE Sungkyunkwan University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.5, no.1, 2005 , pp. 88-93 More about this Journal
Abstract
Web usage mining is a research field for searching potentially useful and valuable information from web log file. Web log file is a simple list of pages that users refer. Therefore, it is not easy to analyze user's current interest field from web log file. This paper presents web usage mining method for finding users' current interest based on fuzzy categories. We consider not only how many times a user visits pages but also when he visits. We describe a user's current interest with a fuzzy interest degree to categories. Based on fuzzy categories and fuzzy interest degrees, we also propose a method to cluster users according to their interests for user modeling. For user clustering, we define a category vector space. Experiments show that our method properly reflects the time factor of users' web visiting as well as the users' visit number.
Keywords
Web usage mining; fuzzy category; user modeling; fuzzy interest;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 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   DOI   ScienceOn
2 R. Cooley, B. Mobasher, J. Srivastava, 'Data Preparation for Mining World Wide Web Browsing Patterns,' Journal of Knowledge and Information System, vol. 1, no. 1, pp.8-19, 1999
3 R. Agrawal, R. Srikant, 'Fast Algorithms for Mining Association Rules,' Proc. of VLDB Conference, pp.487-499, 1994
4 A. Gyenesei, 'A Fuzzy Approach for Mining Quantitative Association Rules,' TUCS Technical Reports, no. 336, 2000
5 R. Cooley, B. Mobasher, J. Srivastava, 'Web mining : Information and Pattern Discovery on the World Wide Web,' Proc. of the 9th IEEE International Conf. on Tools with Artificial Intelligence, pp.61-62, 1997
6 B. Mohasher, R. Cooley, J. Srivastava, 'Automatic personalization based on Web usage mining.,' Communications of the ACM, vol. 43, pp.142-152, 2000   DOI
7 M. Spiliopoulou, 'Web Usage Mining for Web Site Evaluation,' Communications of the ACM, 43, pp.127-134, 2000   DOI
8 O. Zamir O. Etzioni, 'Web document clustering : A feasibility demonstration,' Proc. of the ACM SIGIR Conference, pp.46-53, 1998
9 H. Yi, Y.C. Chen, L.P. Chen, 'Enabling Personalized Recommendation on the Web Based on User Interests and Behaviors,' Proc. of 11th International Workshop, IEEE, pp.1066-1077, 2001   DOI
10 C.C. Aggarwal, S.C. Gates, P.S. Yu, 'On the merites of building categorization systems by supervised clustering,' In Proc. of the ACM SIGKDD conference, pp.352-356, 1999   DOI