A hybrid user model for news story classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 407, Issue , 1999, Pages 99-108

  Billsus, Daniel ^a   Pazzani, Michael J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INDIVIDUAL PERFORMANCE; MODELING COMPONENT; SHORT-TERM INTERESTS; STORY CLASSIFICATION; USER DATA; USER INFORMATION NEED; USER MODELLING;

INTELLIGENT AGENTS;

EID: 84874238848     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-7091-2490-1_10     Document Type: Conference Paper

References (15)

1. Allan, J., Carbonell, J., Doddington, G., Yarnron, J. and Yang Y. (1998). Topic detection and tracking pilot study final report. Proceedings ofthe DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, Virginia.
2. Balabanovic, M. (1998). Learning to Surf: Multiagent Systems for Adaptive Web Page Recommendation. Ph.D. Thesis, Stanford University.
3. Belkin, N. (1997). User modeling in Information Retrieval. Tutorial Overheads, available at http://W\\-W.scils.rutgers.edu!-belkin/um97oh/, Sixth International Conference on User Modeling, Chia Laguna, Sardinia.
4. Belkin, N., Kay, J., Tasso, C. (eds) (1997). Special Issue on User Modeling and Information Filtering. User Modefing and User Adapted Interaction, 7(3).
5. Chiu, B. and Webb, G. (1998). Using decision trees for agent modeling: improving prediction performance. User Modefing and User-Adapted Interaction 8:131-152.
6. Cohen, W. and Hirsh, H. (1998). Joins that generalize: text classification using WHIRL. In Proceedings of the Fourth International Conference on Knowledge Discovery & Data Mining, New York, New York, 169-173.
7. Duda, R., and Hart, P. (1973). Pattern Classification and Scene Analysis. John Wiley & Sons, New York.
8. Lang, K. (1995). News Weeder: learning to filter news. Proceedings of the Twelfth International Conference on Machine Learning. Lake Tahoe, CA, 331-339.
9. Lewis, D. and Gale, W. A. (1994). A sequential algorithm for training text classifiers. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on"Research and Development in Information Retrieval. London, Springer Verlag, 3-12.
10. Maron, M (1961). Automatic indexing: an experimental inquiry. Journal of the Association for Computing Machinery, 8:404-417.
11. McCallum, A. and Nigam, K. (1998). A comparison of event models for naive Bayes text classification. American Association for Artificial Intelligence (AAAI) Workshop on Learning for Text Categorization. Available as Technical Report WS-98-05, AAAl Press.
12. Pazzani M., and Billsus, D. (1997). Learning and revising user profiles: the identification of interesting web sites. Machine Learning 27, 313-331.
13. Salton, G. (1989). Automatie Text Processing. Addison-Wesley.
14. Webb, G. (ed) (1998). Special Issue on Machine Leaming for User Modeling. User Modefing and User-Adapted Interaction, 8(1-2).
15. Yang, Y. (1999). An evaluation of statistical approaches to text categorization. Manuscript submitted for publication.