Learning implicit user interest hierarchy for context in personalization

International Conference on Intelligent User Interfaces, Proceedings IUI

Volumn , Issue , 2003, Pages 101-108

  Kim, Hyoung R ^a   Chan, Philip K ^a  

^a Florida Institute of Technology   (United States)

Author keywords

Clustering algorithm; Concept clustering; User interest hierarchy; User profile

Indexed keywords

ALGORITHMS; LEARNING SYSTEMS; USER INTERFACES; WEB BROWSERS; WEBSITES;

TAXONOMY;

HIERARCHICAL SYSTEMS;

EID: 0038043891     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/604045.604064     Document Type: Conference Paper

References (15)

1. Bellegarda, J.R. Exploiting both local and global constraints for multi-span statistical language modeling, IEEE Proc. Intl. Conf. on Acoustics, Speech, and Signal Processing, vol. 2, 677-680, 1998.
2. Billsus, D., and Pazzani, M.J. A Hybrid User Model for News Story Classification, Conf. User Modeling, 1999.
3. Chan, P.K. A non-invasive learning approach to building web user profiles, KDD-99 Workshop on Web Usage Analysis and User Profiling, 7-12, 1999.
4. Fisher, D.H. Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning 2, 139-172, 1987.
5. Frakes, W.B., and Baeza-Yates, R. Information Retrieval: Data Structures and Algorithms, Prentice-Hall, 1992.
6. Han, J. Data Mining Concepts and Techniques, San Francisco: Morgan Kaufmann Publishers, 2001.
7. He, X., and Ding, C.H.Q., (etc). Automatic topic identification using webpage clustering IEEE ICDM, 2001.
8. James T. M., Frank H. D., Stastistics, San Francisco, Dellen Pub. Co., 1988.
9. Pazzani, M., and Billsus, D. Learning and Revising User Profiles: The Identification of Interesting Web Sites, Machine Learning, 27(3), 313-331, 1997.
10. Perkowitz, M., and Etzioni, O. Towards adaptive Web sites: Conceptual framework and case study, Artificial Intelligence 118, 245-275, 2000.
11. Richardson, M., and Domingos, P. The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank Advances in Neural Information Processing Systems 14, 2002.
12. Russell, S., and Norvig, P. Artificial Intelligence A Modern Approach. Prentice Hall, 74, 1995.
13. Voorhees, E.M. Implementing Agglomerative Hierarchical Clustering Algorithms for use in document retrieval, Information Processing & Management, 22 (6) 465-476, 1986.
14. Zamir, O., and Etzioni, O. Groper: A Dynamic Clustering Interface to Web Search Results, The Eighth International World Wide Web Conference, Toronto, 1999.
15. Zamir, O., and Etzioni, O. Web document clustering: a feasibility demonstration. In Proc. SIGIR-98, 1998.