Tracking changing user interests through prior-learning of context

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

Volumn 2347 LNCS, Issue , 2002, Pages 223-232

  Koychev, Ivan ^a  

^a AiS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

HYPERMEDIA SYSTEMS; WEBSITES; ALGORITHMS; WORLD WIDE WEB;

DATA SET; LEARNING LEVELS; PREDICTIVE ACCURACY; USER INTERESTS;

LEARNING ALGORITHMS;

EID: 26944454094     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47952-x_24     Document Type: Conference Paper

References (17)

1. Billsus, D., and Pazzani, M. J.: A Hybrid User Model for News Classification. In Kay J. (ed.), UM99: Proceedings of the Seventh International Conference on User Modeling, Lecture Notes in Computer Science, Springer-Verlag (1999) pp. 99-108.
2. Blum, A.: Empirical Support of Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain. Machine Learning 26 (1997): 5-23.
3. Brusikovsky, P. Adaptive Hypermedia. User Modeling and User-Adapted Interaction 11 (2001) 87-110.
4. Chiu, B. and Webb, G.: Using Decision Trees for Agent Modeling: Improving Prediction Performance. User Modeling and User-Adapted Interaction 8 (1/2) (1998) 131-152.
5. Grabtree, I. and Soltysiak, S.: Identifying and Tracking Changing Interests. International Journal of Digital Libraries vol. 2 (1998) 38-53.
6. Harries, M. and Sammut, C. Extracting Hidden Context. Machine Learning 32 (1998) 101-126.
7. Klingenberg, R. and Renz, I.: Adaptive information filtering: learning in the presence of concept drift. AAAI/ICML-98 Workshop on Learning for Text Categorization, TR WS-98-05, Madison, WI, (1998).
8. Kobsa, A., Koenemann, J. and Pohl, W.: Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Engineering Review, 16(2) (2001) 111-155.
9. Koychev, I. and Schwab, I.: Adaptation to Drifting User's Intersects - Proceedings ECML2000/MLnet workshop: ML in the New Information Age, Barcelona, Spain, (2000) pp. 39-45.
10. Maloof, M. and Michalski, R.: Selecting examples for partial memory learning. Machine Learning 41 (2000) 27-52.
11. Mitchell, T., Caruana, R., Freitag, D., McDermott, J. and Zabowski, D.: Experience with a Learning Personal Assistant. Communications of the ACM 37(7) (1994) 81-91.
12. Quinlan, R.: Induction of Decision Trees. Machine Learning 1 (1986) 81-106.
13. Schlimmer, J. and Granger, R.: Incremental Learning from Noisy Data. Machine Learning 3, Kluwer Academic Publishers (1986), 317-357.
14. Webb, G. and Kuzmycz, M.: Feature-based modelling: a methodology for producing coherent, consistent, dynamically changing models of agents' competencies. User Modeling and User-Adapted Interaction 5(2) (1996) 117-150.
15. Webb, G. Pazzani, M. and Billsus, D. Machine Learning for user modeling. User Modeling and User-Adaptive Interaction 11 (2001) 19-29.
16. Widmer, G.: Tracking Changes through Meta-Learning. Machine Learning 27 (1997) 256-286.
17. Widmer, G. and Kubat, M.: Learning in the presence of concept drift and hidden contexts: Machine Learning 23 (1996) 69-101.